National Instruments IMAQTM User Manual

Download

IMAQ

IMAQ Vision for Visual Basic User Manual

August 2004 Edition

Part Number 371257A-01

Support

Worldwide Technical Support and Product Information

ni.com

National Instruments Corporate Headquarters

11500 North Mopac Expressway Austin, Texas 78759-3504 USA Tel: 512 683 0100

Worldwide Offices

Australia 1800 300 800, Austria 43 0 662 45 79 90 0, Belgium 32 0 2 757 00 20, Brazil 55 11 3262 3599, Canada (Calgary) 403 274 9391, Canada (Ottawa) 613 233 5949, Canada (Québec) 450 510 3055, Canada (Toronto) 905 785 0085, Canada (Vancouver) 604 685 7530, China 86 21 6555 7838, Czech Republic 420 224 235 774, Denmark 45 45 76 26 00, Finland 385 0 9 725 725 11, France 33 0 1 48 14 24 24, Germany 49 0 89 741 31 30, India 91 80 51190000, Israel 972 0 3 6393737, Italy 39 02 413091, Japan 81 3 5472 2970, Korea 82 02 3451 3400, Malaysia 603 9131 0918, Mexico 01 800 010 0793, Netherlands 31 0 348 433 466, New Zealand 0800 553 322, Norway 47 0 66 90 76 60, Poland 48 22 3390150, Portugal 351 210 311 210, Russia 7 095 783 68 51, Singapore 65 6226 5886, Slovenia 386 3 425 4200, South Africa 27 0 11 805 8197, Spain 34 91 640 0085, Sweden 46 0 8 587 895 00, Switzerland 41 56 200 51 51, Taiwan 886 2 2528 7227, Thailand 662 992 7519, United Kingdom 44 0 1635 523545

For further support information, refer to the Technical Support and Professional Services appendix. To comment on National Instruments documentation, refer to the National Instruments Web site at ni.com/info and enter the info code feedback.

Important Information

Warranty

The media on which you receive National Instruments software are warranted not to fail to execute programming instructions, due to defects in materials and workmanship, for a period of 90 days from date of shipment, as evidenced by receipts or other documentation. National Instruments will, at its option, repair or replace software media that do not execute programming instructions if National Instruments receives notice of such defects during the warranty period. National Instruments does not warrant that the operation of the software shall be uninterrupted or error free.

A Return Material Authorization (RMA) number must be obtained from the factory and clearly marked on the outside of the package before any equipment will be accepted for warranty work. National Instruments will pay the shipping costs of returning to the owner parts which are covered by warranty.

National Instruments believes that the information in this document is accurate. The document has been carefully reviewed for technical accuracy. In the event that technical or typographical errors exist, National Instruments reserves the right to make changes to subsequent editions of this document without prior notice to holders of this edition. The reader should consult National Instruments if errors are suspected. In no event shall National Instruments be liable for any damages arising out of or related to this document or the information contained in it.

XCEPT AS SPECIFIED HEREIN, NATIONAL INSTRUMENTS MAKES NO WARRANTIES, EXPRESS OR IMPLIED, AND SPECIFICALLY DISCLAIMS ANY WAR RANTY OF

MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE . CUSTOMER’S RIGHT TO RECOVER DAMAGES CAUSED BY FAULT OR NEGLIGENCE ON THE PART OF

ATIONAL INSTRUMENTS SHALL BE LIMITED TO THE AMOUNT THERETOFORE PAID BY THE CUSTOMER. NATIONAL INSTRUMENTS WILL NOT BE LIABLE FOR DAMAGES RESULTING FROM LOSS OF DATA, PROFITS, USE OF PRODUCTS, OR INCIDENTAL OR CONSEQUENTIAL DAMAGES, EVEN IF ADVISED OF THE POSS IBILITY THEREOF. This limitation of the liability of National Instruments will apply regardless of the form of action, whether in contract or tort, including

negligence. Any action against National Instruments must be brought within one year after the cause of action accrues. National Instruments shall not be liable for any delay in performance due to causes beyond its reasonable control. The warranty provided herein does not cover damages, defects, malfunctions, or service failures caused by owner’s failure to follow the National Instruments installation, operation, or maintenance instructions; owner’s modification of the product; owner’s abuse, misuse, or negligent acts; and power failure or surges, fire, flood, accident, actions of third parties, or other events outside reasonable control.

Copyright

Under the copyright laws, this publication may not be reproduced or transmitted in any form, electronic or mechanical, including photocopying, recording, storing in an information retrieval system, or translating, in whole or in part, without the prior written consent of National Instruments Corporation.

Trademarks

CVI™, IMAQ™, LabVIEW™, National Instruments™, National Instruments Alliance Partner™, NI™, ni.com™, NI Developer Zone™, and

™

NI-IMAQ

Product and company names mentioned herein are trademarks or trade names of their respective companies.

Members of the National Instruments Alliance Partner Program are business entities independent from National Instruments and have no agency, partnership, or joint-venture relationship with National Instruments.

are trademarks of National Instruments Corporation.

WARNING REGARDING USE OF NATIONAL INSTRUMENTS PRODUCTS

(1) NATIONAL INSTRUMENTS PRODUCTS ARE NOT DESIGNED WITH COMPONENTS AND TESTING FOR A LEVEL OF RELIABILITY SUITABLE FOR USE IN OR IN CONNECTION WITH SURGICAL IMPLANTS OR AS CRITICAL COMPONENTS IN ANY LIFE SUPPORT SYSTEMS WHOSE FAILURE TO PERFORM CAN REASONABLY BE EXPECTED TO CAUSE SIGNIFICANT INJURY TO A HUMAN.

(2) IN ANY APPLICATION, INCLUDING THE ABOVE, RELIABILITY OF OPERATION OF THE SOFTWARE PRODUCTS CAN BE IMPAIRED BY ADVERSE FACTORS, INCLUDING BUT NOT LIMITED TO FLUCTUATIONS IN ELECTRICAL POWER SUPPLY, COMPUTER HARDWARE MALFUNCTIONS, COMPUTER OPERATING SYSTEM SOFTWARE FITNESS, FITNESS OF COMPILERS AND DEVELOPMENT SOFTWARE USED TO DEVELOP AN APPLICATION, INSTALLATION ERRORS, SOFTWARE AND HARDWARE COMPATIBILITY PROBLEMS, MALFUNCTIONS OR FAILURES OF ELECTRONIC MONITORING OR CONTROL DEVICES, TRANSIENT FAILURES OF ELECTRONIC SYSTEMS (HARDWARE AND/OR SOFTWARE), UNANTICIPATED USES OR MISUSES, OR ERRORS ON THE PART OF THE USER OR APPLICATIONS DESIGNER (ADVERSE FACTORS SUCH AS THESE ARE HEREAFTER COLLECTIVELY TERMED “SYSTEM FAILURES”). ANY APPLICATION WHERE A SYSTEM FAILURE WOULD CREATE A RISK OF HARM TO PROPERTY OR PERSONS (INCLUDING THE RISK OF BODILY INJURY AND DEATH) SHOULD NOT BE RELIANT SOLELY UPON ONE FORM OF ELECTRONIC SYSTEM DUE TO THE RISK OF SYSTEM FAILURE. TO AVOID DAMAGE, INJURY, OR DEATH, THE USER OR APPLICATION DESIGNER MUST TAKE REASONABLY PRUDENT STEPS TO PROTECT AGAINST SYSTEM FAILURES, INCLUDING BUT NOT LIMITED TO BACK-UP OR SHUT DOWN MECHANISMS. BECAUSE EACH END-USER SYSTEM IS CUSTOMIZED AND DIFFERS FROM NATIONAL INSTRUMENTS' TESTING PLATFORMS AND BECAUSE A USER OR APPLICATION DESIGNER MAY USE NATIONAL INSTRUMENTS PRODUCTS IN COMBINATION WITH OTHER PRODUCTS IN A MANNER NOT EVALUATED OR CONTEMPLATED BY NATIONAL INSTRUMENTS, THE USER OR APPLICATION DESIGNER IS ULTIMATELY RESPONSIBLE FOR VERIFYING AND VALIDATING THE SUITABILITY OF NATIONAL INSTRUMENTS PRODUCTS WHENEVER NATIONAL INSTRUMENTS PRODUCTS ARE INCORPORATED IN A SYSTEM OR APPLICATION, INCLUDING, WITHOUT LIMITATION, THE APPROPRIATE DESIGN, PROCESS AND SAFETY LEVEL OF SUCH SYSTEM OR APPLICATION.

About This Manual

Conventions ...................................................................................................................ix

Related Documentation..................................................................................................x

Chapter 1 Introduction to IMAQ Vision

About IMAQ Vision ......................................................................................................1-1

Documentation and Examples .......................................................................................1-1

IMAQ Vision for Visual Basic Organization ................................................................1-2

cwimaq.ocx......................................................................................................1-3

CWIMAQ Control ............................................................................1-3

CWIMAQVision Control..................................................................1-3

CWIMAQViewer Control.................................................................1-3

niocr.ocx ..........................................................................................................1-4

NIOCR control..................................................................................1-4

cwmv.ocx.........................................................................................................1-4

CWMachineVision control ...............................................................1-4

ActiveX Objects ..............................................................................................1-5

Creating IMAQ Vision Applications.............................................................................1-5

Chapter 2 Getting Measurement-Ready Images

Set Up Your Imaging System ........................................................................................2-1

Calibrate Your Imaging System ....................................................................................2-2

Create an Image .............................................................................................................2-2

Acquire or Read an Image .............................................................................................2-4

Acquiring an Image .........................................................................................2-4

One-Shot Acquisition........................................................................2-4

Continuous Acquisition.....................................................................2-5

Reading a File..................................................................................................2-6

Converting an Array to an Image ....................................................................2-6

Display an Image ...........................................................................................................2-6

Attach Calibration Information......................................................................................2-7

Analyze an Image ..........................................................................................................2-7

Contents

Improve an Image.......................................................................................................... 2-9

Lookup Tables................................................................................................. 2-9

Filters .............................................................................................................. 2-9

Convolution Filter............................................................................. 2-10

Nth Order Filter ................................................................................ 2-10

Grayscale Morphology.................................................................................... 2-10

FFT.................................................................................................................. 2-11

Complex Image Operations .............................................................. 2-12

Chapter 3 Making Grayscale and Color Measurements

Define Regions of Interest............................................................................................. 3-1

Defining Regions Interactively ....................................................................... 3-1

Defining Regions Programmatically............................................................... 3-5

Defining Regions with Masks......................................................................... 3-6

Measure Grayscale Statistics......................................................................................... 3-6

Measure Color Statistics................................................................................................ 3-7

Comparing Colors ........................................................................................... 3-9

Learning Color Information ............................................................................ 3-9

Specifying the Color Information to Learn ...................................... 3-10

Using the Entire Image ..................................................................... 3-10

Choosing a Color Representation Sensitivity ................................... 3-12

Ignoring Learned Colors................................................................... 3-13

Chapter 4 Performing Particle Analysis

Create a Binary Image ................................................................................................... 4-1

Improve the Binary Image............................................................................................. 4-2

Removing Unwanted Particles........................................................................ 4-3

Separating Touching Particles ........................................................................ 4-3

Improving Particle Shapes .............................................................................. 4-4

Make Particle Measurements ........................................................................................ 4-4

Chapter 5 Performing Machine Vision Tasks

Locate Objects to Inspect .............................................................................................. 5-2

Using Edge Detection to Build a Coordinate Transformation ........................ 5-3

Using Pattern Matching to Build a Coordinate Transformation ..................... 5-5

Choosing a Method to Build the Coordinate Transformation......................... 5-7

IMAQ Vision for Visual Basic User Manual vi ni.com

Contents

Set Search Areas ............................................................................................................5-8

Defining Regions Interactively........................................................................5-8

Defining Regions Programmatically ...............................................................5-9

Find Measurement Points ..............................................................................................5-9

Finding Features Using Edge Detection..........................................................5-9

Finding Lines or Circles....................................................................5-10

Finding Edge Points Along One Search Contour .............................5-11

Finding Edge Points Along Multiple Search Contours.....................5-12

Finding Points Using Pattern Matching ..........................................................5-12

Defining and Creating Effective Template Images...........................5-13

Training the Pattern Matching Algorithm.........................................5-15

Defining a Search Area .....................................................................5-16

Setting Matching Parameters and Tolerances...................................5-17

Testing the Search Algorithm on Test Images..................................5-18

Using a Ranking Method to Verify Results ......................................5-19

Finding Points Using Color Pattern Matching ................................................5-19

Defining and Creating Effective Color Template Images ................5-20

Training the Color Pattern Matching Algorithm...............................5-21

Defining a Search Area .....................................................................5-22

Setting Matching Parameters and Tolerances...................................5-23

Testing the Search Algorithm on Test Images..................................5-25

Finding Points Using Color Location..............................................................5-25

Convert Pixel Coordinates to Real-World Coordinates.................................................5-26

Make Measurements ......................................................................................................5-26

Distance Measurements...................................................................................5-26

Analytic Geometry Measurements ..................................................................5-27

Instrument Reader Measurements ...................................................................5-28

Identify Parts Under Inspection .....................................................................................5-28

Classifying Samples ........................................................................................5-28

Reading Characters..........................................................................................5-29

Reading Barcodes............................................................................................5-29

Read 1D Barcodes.............................................................................5-29

Read Data Matrix Barcode................................................................5-30

Read PDF417 Barcode......................................................................5-31

Display Results ..............................................................................................................5-31

Contents

Chapter 6 Calibrating Images

Perspective and Nonlinear Distortion Calibration......................................................... 6-1

Defining a Calibration Template..................................................................... 6-2

Defining a Reference Coordinate System ....................................................... 6-3

Learning Calibration Information ................................................................... 6-5

Specifying Scaling Factors ............................................................... 6-6

Choosing a Region of Interest .......................................................... 6-6

Choosing a Learning Algorithm ....................................................... 6-6

Using the Learning Score ................................................................. 6-7

Learning the Error Map .................................................................... 6-8

Learning the Correction Table..........................................................6-8

Setting the Scaling Mode.................................................................. 6-8

Calibration Invalidation.................................................................... 6-9

Simple Calibration......................................................................................................... 6-9

Save Calibration Information ........................................................................................ 6-10

Attach Calibration Information ..................................................................................... 6-10

Appendix A Technical Support and Professional Services

Glossary

Index

IMAQ Vision for Visual Basic User Manual viii ni.com

About This Manual

The IMAQ Vision for Visual Basic User Manual is intended for engineers and scientists who have knowledge of Microsoft Visual Basic and need to create machine vision and image processing applications using Visual Basic objects. The manual guides you through tasks beginning with setting up the imaging system to taking measurements.

Conventions

The following conventions appear in this manual:

» The » symbol leads you through nested menu items and dialog box options

to a final action. The sequence File»Page Setup»Options directs you to pull down the File menu, select the Page Setup item, and select Options from the last dialog box.

This icon denotes a tip, which alerts you to advisory information.

This icon denotes a note, which alerts you to important information.

bold Bold text denotes items that you must select or click in the software, such

as menu items and dialog box options. Bold text also denotes parameter names.

italic Italic text denotes variables, emphasis, a cross reference, or an introduction

to a key concept. This font also denotes text that is a placeholder for a word or value that you must supply.

monospace Text in this font denotes text or characters that you should enter from the

keyboard, sections of code, programming examples, and syntax examples. This font is also used for the proper names of disk drives, paths, directories, programs, subprograms, subroutines, device names, functions, operations, variables, filenames, and extensions.

About This Manual

Note You must have the NI-IMAQ driver software installed on the target system to use the

CWIMAQ control. For information about NI-IMAQ, refer to the NI-IMAQ User Manual that came with the IMAQ device.

Chapter 1 Introduction to IMAQ Vision

cwimaq.ocx contains the following three ActiveX controls and a

collection of ActiveX objects: CWIMAQ, CWIMAQVision, and CWIMAQViewer. Refer to the ActiveX Objects section for information about the ActiveX objects.

CWIMAQ Control

Use this control to configure and perform an acquisition from the IMAQ device. The CWIMAQ control has property pages that allow you to modify various parameters to configure the acquisition and gather information about the IMAQ device. Most of the functionality available from the property pages during design time is also available through the properties of the CWIMAQ control during run-time. The control has methods that allow you to perform and control acquisitions, as well.

CWIMAQVision Control

Use this control to analyze and process images and their related data. The CWIMAQVision control provides methods for reading and writing images to and from files, analyzing images, and performing a variety of image processing algorithms on images.

CWIMAQViewer Control

Use this control to display images and provide the interface through which the user will interact with the displayed image. This includes the ability to zoom and pan images and to draw regions of interest (ROIs) on an image. The CWIMAQViewer control has property pages that allow you to configure the viewer’s appearance and behavior during design time as well as properties that you can configure during run-time. The control has methods that allow you to attach images to and detach images from the viewer for display purposes.

Note The CWIMAQViewer control is referred to as a viewer in the remainder of this

document.

Chapter 1 Introduction to IMAQ Vision

niocr.ocx

niocr.ocx provides one ActiveX control and a collection of ActiveX

objects you use in a machine vision application to perform optical character recognition (OCR).

NIOCR control

Use this control to perform OCR, which is the process by which the machine vision software reads text and/or characters in an image. OCR consists of the following two procedures:

• Training characters

• Reading characters

Training characters is the process by which you teach the machine vision software the types of characters and/or patterns you want to read in the image during the reading procedure. You can use IMAQ Vision to train any number of characters, creating a character set, which is the set of characters that you later compare with objects during the reading procedure. You store the character set you create in a character set file. Training might be a one-time process, or it might be a process you repeat several times, creating several character sets to broaden the scope of characters you want to detect in an image.

Reading characters is the process by which the machine vision application you create analyzes an image to determine if the objects match the characters you trained. The machine vision application reads characters in an image using the character set that you created when you trained characters.

cwmv.ocx

cwmv.ocx contains one ActiveX control and a collection of ActiveX

objects. Refer to the ActiveX Objects section for more information about ActiveX objects.

CWMachineVision control

Use this control to perform high-level machine vision tasks, such as measuring distances. This control is written entirely in Visual Basic using the methods on the CWIMAQVision and CWIMAQViewer controls. The source code for the CWMachineVision control is included in the product. For more information about CWMachineVision methods, refer to Chapter 5, Performing Machine Vision Tasks.

IMAQ Vision for Visual Basic User Manual 1-4 ni.com

Tip Refer to the source code of the CWMachineVision control for an example of how to

use the CWIMAQVision methods.

ActiveX Objects

Note ActiveX objects in cwimaq.ocx have a CWIMAQ prefix, objects in niocr.ocx

have an NIOCR prefix, and objects in

If you intend to develop an application in Visual C++, National Instruments

Tip

recommends that you use IMAQ Vision for LabWindows/CVI. However, if you decide to use IMAQ Vision for Visual Basic to develop applications for Visual C++, you can create objects using the respective Create methods on the CWIMAQVision control or CWMachineVision control. For example, to create a CWIMAQImage object, use the

CWIMAQVision.CreateCWIMAQImage method.

Chapter 1 Introduction to IMAQ Vision

Use the objects to group related input parameters and output parameters to certain methods, thus reducing the number of parameters that you actually need to pass to those methods.

cwmv.ocx have a CWMV prefix.

You must create an ActiveX object before you can use it. You can use the

New keyword in Visual Basic to create these objects. For example, use the

following syntax to create and store an image in a variable named

Dim image As New CWIMAQImage

image:

Creating IMAQ Vision Applications

Figures 1-1 and 1-2 illustrate the steps for creating an application with IMAQ Vision. Figure 1-1 describes the general steps for designing a Vision application. The last step in Figure 1-1 is expanded upon in Figure 1-2. You can use a combination of the items in the last step to create a IMAQ Vision application. For more information about items in either diagram, refer to the corresponding chapter listed beside the item.

Note Diagram items enclosed with dashed lines are optional steps.

Chapter 1 Introduction to IMAQ Vision

Set Up Your Imaging System

Chapter 2:

Measurement-Ready

Getting

Images

Calibrate Your Imaging System

Create an Image

Acquire or Read an Image

Display an Image

Attach Calibration Information

Analyze an Image

Improve an Image

Make Measurements or Identify Objects

in an Image Using

Grayscale or Color Measurements, and/or

Particle Analysis, and/or

Machine Vision

Chapter 6: Calibrating Images

Figure 1-1. General Steps for Designing a Vision Application

IMAQ Vision for Visual Basic User Manual 1-6 ni.com

Chapter 1 Introduction to IMAQ Vision

Making Grayscale and Color

Chapter 4:

Performing

Particle

Analysis

Measurements

Chapter 3:

Grayscale Statistics

Create a Binary Image

Improve a Binary Image

Make Particle Measurements

Define Regions of Interest

Measure

Color Statistics

Find Measurement Points

Convert Pixel Coordinates to

Real-World Coordinates

Make Measurements

Measure

Locate Objects to Inspect

Set Search Areas

Identify Parts Under Inspection

Classify Objects

Display Results

Read

Characters

Read

Symbologies

Chapter 5: Performing Machine Vision Tasks

Figure 1-2. Inspection Steps for Building a Vision Application

Getting Measurement-Ready Images

This chapter describes how to set up an imaging system, acquire and display an image, analyze the image, and prepare the image for additional processing.

Set Up Your Imaging System

Before you acquire, analyze, and process images, you must set up an imaging system. The manner in which you set up the system depends on the imaging environment and the type of analysis and processing you need to do. Your imaging system should produce images with high enough quality so that you can extract the information you need from the images.

Follow the guidelines below to set up an imaging system.

1. Determine the type of equipment you need based on the space constraints and the size of the object you need to inspect. For more information, refer to Chapter 3, System Setup and Calibration, of the IMAQ Vision Concepts Manual.

a. Make sure the camera sensor is large enough to satisfy the

minimum resolution requirement.

b. Make sure the lens has a depth of field high enough to keep all of

the objects in focus regardless of their distance from the lens. Also, make sure the lens has a focal length that meets your needs.

c. Make sure the lighting provides enough contrast between the

object under inspection and the background for you to extract the information you need from the image.

2. Position the camera so that it is parallel to the object under inspection. If the camera acquires images of the object from an angle, perspective errors occur. Even though you can compensate for these errors with software, NI recommends that you use a perpendicular inspection angle to obtain the fastest and most accurate results.

3. Select an image acquisition device that meets your needs. National Instruments offers several image acquisition devices, such as analog

Chapter 2 Getting Measurement-Ready Images

color and monochrome devices as well as digital devices. Visit

ni.com/imaq for more information about IMAQ devices.

4. Configure the driver software for the image acquisition device. If you have a National Instruments image acquisition device, configure the NI-IMAQ driver software through Measurement & Automation Explorer (MAX). Open MAX by double-clicking the Measurement & Automation Explorer icon on the desktop. For more information, refer to the NI-IMAQ User Manual and the Measurement & Automation Explorer Help for IMAQ.

Calibrate Your Imaging System

After you set up the imaging system, you may want to calibrate the system. Calibrate the imaging system to assign real-world coordinates to pixel coordinates and compensate for perspective and nonlinear errors inherent in the imaging system.

Perspective errors occur when the camera axis is not perpendicular to the object under inspection. Nonlinear distortion may occur from aberrations in the camera lens. Perspective errors and lens aberrations cause images to appear distorted. This distortion displaces information in an image, but it does not necessarily destroy the information in the image.

Use simple calibration if you want only to assign real-world coordinates to pixel coordinates. Use perspective and nonlinear distortion calibration if you need to compensate for perspective errors and nonlinear lens distortion. For detailed information about calibration, refer to Chapter 6, Calibrating

Images.

Create an Image

The CWIMAQImage object encapsulates all the information required to represent an image.

Note CWIMAQImage is referred to as an image in the remainder of this document.

An image can be one of many types, depending on the data it stores. The following image types are valid:

•8-bit

• 16-bit

• Single-precision floating point

IMAQ Vision for Visual Basic User Manual 2-2 ni.com

Chapter 2 Getting Measurement-Ready Images

• Complex

• 32-bit RGB

• 32-bit HSL

• 64-bit RGB

When you create an image, it is an 8-bit image by default. You can set the

Type property on the image object to change the image type.

When you create an image, no memory is allocated to store the image pixels. IMAQ Vision methods automatically allocate the appropriate amount of memory when the image size is modified. For example, methods that acquire or resample an image alter the image size, so they allocate the appropriate memory space for the image pixels.

Most methods belonging to the IMAQ Vision library require an input of one or more image objects. The number of images a method takes depends on the image processing function and the type of image you want to use.

IMAQ Vision methods that analyze the image but do not modify the image contents require the input of one source image. Methods that process the contents of the image require one or more source images and a destination image. Exceptions to the preceding statements are methods that take a mask image as input.

The presence of a

MaskImage parameter indicates that the processing or

analysis is dependent on the contents of the mask image. The only pixels in the source image that are processed are those whose corresponding pixels in the mask image are non-zero. If a mask image pixel is 0, the corresponding source image pixel is not processed or analyzed. The mask image must be an 8-bit image.

If you want to apply a processing or analysis method to the entire image, do not supply the optional mask image. Using the same image for both the source image and mask image also has the same effect as not using the mask image, except in this case the source image must be an 8-bit image.

Most operations between two images require that the images have the same type and size. However, arithmetic operations work between two different types of images. For example, an arithmetic operation between an 8-bit image and 16-bit image results in a 16-bit image.

Chapter 2 Getting Measurement-Ready Images

Acquire or Read an Image

After you create an image, you can acquire an image into the imaging system in one of the following three ways:

• Acquire an image with a camera through the image acquisition device.

• Load an image from a file stored on the computer.

• Convert the data stored in a 2D array to an image.

Methods that acquire images, load images from file, or convert data from a 2D array automatically allocate the memory space required to accommodate the image data.

Acquiring an Image

Use the CWIMAQ control to acquire images with a National Instruments IMAQ device. You can use IMAQ Vision for Visual Basic to perform one-shot and continuous acquisitions. You can choose the acquisition type during design time by setting the value of the Acquisition Type combo box to One-Shot or Continuous. The Acquisition Type combo box is located on the Acquisition property page of the CWIMAQ control. You can set the value at run-time by setting the

cwimaqAcquisitionOneShot or cwimaqAcquisitionContinuous.

CWIMAQ.AcquisitionType property to

One-Shot Acquisition

Use a one-shot acquisition to start an acquisition, perform the acquisition, and stop the acquisition using a single method. The number of frames acquired is equal to the number of images in the images collection. Use the

CWIMAQ.AcquireImage method to perform this operation synchronously.

Use the For information about synchronous and asynchronous acquisitions, refer to the NI-IMAQ User Manual.

If you want to acquire a single field or frame into a buffer, set the image count to 1. This operation is also referred to as a snap. Use a snap for low-speed or single capture applications. The following code illustrates a synchronous snap:

Private Sub Start_Click()

End Sub

IMAQ Vision for Visual Basic User Manual 2-4 ni.com

CWIMAQ.Start method to perform this operation asynchronously.

CWIMAQ1.AcquisitionType = cwimaqAcquisitionOneShot

CWIMAQ1.AcquireImage

Chapter 2 Getting Measurement-Ready Images

If you want to acquire multiple frames, set the image count to the number of frames you want to acquire. This operation is called a sequence. Use a sequence for applications that process multiple images. The following code illustrates an asynchronous sequence, where

numberOfImages is the

number of images that you want to process:

Private Sub Start_Click()

CWIMAQ1.AcquisitionType = cwimaqAcquisitionOneShot

CWIMAQ1.Images.RemoveAll

CWIMAQ1.Images.Add numberOfImages

CWIMAQ1.Start

End Sub

Continuous Acquisition

Use a continuous acquisition to start an acquisition and continuously acquire frames into the image buffers, and then explicitly stop the acquisition. Use the the

CWIMAQ.Stop method to stop the acquisition. If you use a single buffer

for the acquisition, this operation is called a grab. The following code illustrates a grab:

Private Sub Start_Click()

CWIMAQ1.AcquisitionType=_

cwimaqAcquisitionContinuous

CWIMAQ1.Start

End Sub

CWIMAQ.Start method to start the acquisition. Use

Private Sub Stop_Click()

CWIMAQ1.Stop

End Sub

A ring operation uses multiple buffers for the acquisition. Use a ring for high-speed applications that require processing on every image. The following code illustrates a ring, where

numberOfImages is the number of

images that you want to process:

Private Sub Start_Click()

CWIMAQ1.AcquisitionType =_

cwimaqAcquisitionContinuous

CWIMAQ1.Images.RemoveAll

CWIMAQ1.Images.Add numberOfImages

CWIMAQ1.Start

End Sub

Chapter 2 Getting Measurement-Ready Images

Private Sub Stop_Click()

CWIMAQ1.Stop

End Sub

Reading a File

Use the CWIMAQVision.ReadImage method to open and read data from a file stored on the computer into the image reference. You can read from image files stored in several standard formats, such as BMP, TIFF, JPEG, PNG, and AIPD. In all cases, the software automatically converts the pixels it reads into the type of image you pass in.

Use the image file containing additional information, such as calibration information, template information for pattern matching, or overlay information. For more information about pattern matching templates and overlays, refer to Chapter 5, Performing Machine Vision Tasks.

You also can use the to retrieve image properties—image size, pixel depth, recommended image type, and calibration units—without actually reading all the image data.

CWIMAQVision.ReadImageAndVisionInfo method to open an

Converting an Array to an Image

Use the CWIMAQImage.ArrayToImage method to convert an array to an image. You also can use the convert an image to an array.

Display an Image

Display an image using the CWIMAQViewer control. Use

CWIMAQViewer.Attach to attach the image you want the viewer

to display. When you attach an image to a viewer, the image automatically updates the viewer whenever an operation modifies the contents of the image. You can access the image attached to the viewer using the

CWIMAQViewer.Image property. Before you attach an image to the

viewer, the viewer already has an image attached by default. Therefore, the viewer has an image attached to it at all times. You can use the attached image as either a source image, destination image, or both using the

CWIMAQViewer.Image property.

CWIMAQVision.GetFileInformation method

CWIMAQImage.ImageToArray method to

You can use the CWIMAQPalette object associated with the viewer. Use the CWIMAQPalette object to programmatically apply a color palette to

IMAQ Vision for Visual Basic User Manual 2-6 ni.com

CWIMAQViewer.Palette property to access the

the viewer. You can set the CWIMAQPalette.Type property to apply predefined color palettes. For example, if you need to display a binary image—an image that contains particle regions with pixel values of 1 and a background region with pixel values of 0—set the

cwimaqPaletteBinary. For more information about color palettes, refer

to Chapter 2, Display, of the IMAQ Vision Concepts Manual.

You also can set a default palette during design time using the Menu property page. Users can change the color palette during run time by using the right-click menu on the viewer.

Attach Calibration Information

If you want to attach the calibration information of the current setup to each image you acquire, use

CWIMAQVision.SetCalibrationInformation. This method takes

in a source image that contains the calibration information and a destination image that you want to calibrate. The output image is the inspection image with the calibration information attached to it. For detailed information about calibration, refer to Chapter 6, Calibrating

Images.

Chapter 2 Getting Measurement-Ready Images

Type property to

Note Because calibration information is part of the image, it is propagated throughout

the processing and analysis of the image. Methods that modify the image size, such as geometrical transforms, void the calibration information. Use

CWIMAQVision.WriteImageAndVisionInfo to save the image and all of the

attached calibration information to a file.

Analyze an Image

When you acquire and display an image, you may want to analyze the contents of the image for the following reasons:

• To determine if the image quality is high enough for the inspection task.

• To obtain the values of parameters that you want to use in processing methods during the inspection process.

The histogram and line profile tools can help you analyze the quality of the images.

Chapter 2 Getting Measurement-Ready Images

Use CWIMAQVision.Histogram2 to analyze the overall grayscale distribution in the image. Use the histogram of the image to analyze two important criteria that define the quality of an image—saturation and contrast. If the image does not have enough light, the majority of the pixels will have low intensity values, which appear as a concentration of peaks on the left side of the histogram. If the image has too much light, the majority of the pixels will have a high intensity values, which appear as a concentration of peaks on the right side of the histogram. If the image has an appropriate amount of contrast, the histogram will have distinct regions of pixel concentrations. Use the histogram information to decide if the image quality is high enough to separate objects of interest from the background.

If the image quality meets your needs, use the histogram to determine the range of pixel values that correspond to objects in the image. You can use this range in processing methods, such as determining a threshold range during particle analysis.

If the image quality does not meet your needs, try to improve the imaging conditions to get the appropriate image quality. You may need to re-evaluate and modify each component of the imaging setup: lighting equipment and setup, lens tuning, camera operation mode, and acquisition board parameters. If you reach the best possible conditions with the setup but the image quality still does not meet your needs, try to improve the image quality using the image processing techniques described in the

Improve an Image section of this chapter.

Use

CWIMAQVision.LineProfile2 to get the pixel distribution along a

line in the image, or use

CWIMAQVision.RegionsProfile to get the

pixel distribution along a one-dimensional path in the image. By looking at the pixel distribution, you can determine if the image quality is high enough to provide you with sharp edges at object boundaries. Also, you can determine if the image is noisy, and identify the characteristics of the noise.

If the image quality meets your needs, use the pixel distribution information to determine some parameters of the inspection methods you want to use. For example, use the information from the line profile to determine the strength of the edge at the boundary of an object. You can input this information into

CWIMAQVision.FindEdges2 to find the edges

of objects along the line.

IMAQ Vision for Visual Basic User Manual 2-8 ni.com

Improve an Image

Using the information you gathered from analyzing the image, you may want to improve the quality of the image for inspection. You can improve the image with lookup tables, filters, grayscale morphology, and Fast Fourier transforms (FFT).

Lookup Tables

Apply lookup table (LUT) transformations to highlight image details in areas containing significant information at the expense of other areas. A LUT transformation converts input grayscale values in the source image into other grayscale values in the transformed image. IMAQ Vision provides four methods that directly or indirectly apply lookup tables to images:

•

Chapter 2 Getting Measurement-Ready Images

CWIMAQVision.MathLookup—Converts the pixel values of an

image by replacing them with values from a predefined lookup table. IMAQ Vision has seven predefined lookup tables based on mathematical transformations. For more information about these lookup tables, refer to Chapter 5, Image Processing, in the IMAQ Vision Concepts Manual.

CWIMAQVision.UserLookup—Converts the pixel values of an

image by replacing them with values from a user-defined lookup table.

CWIMAQVision.Equalize2—Distributes the grayscale values

evenly within a given grayscale range. Use this method to increase the contrast in images containing few grayscale values.

CWIMAQVision.Inverse—Inverts the pixel intensities of an image

to compute the negative of the image. For example, use this method before applying an automatic threshold to the image if the background pixels are brighter than the object pixels.

Filters

Filter the image when you need to improve the sharpness of transitions in the image or increase the overall signal-to-noise ratio of the image. You can choose either a lowpass or highpass filter, depending on your needs.

Lowpass filters remove insignificant details by smoothing the image, removing sharp details, and smoothing the edges between the objects and the background. You can use your own lowpass filter with

CWIMAQVision.NthOrder.

CWIMAQVision.LowPass or define

CWIMAQVision.Convolute or

Chapter 2 Getting Measurement-Ready Images

Highpass filters emphasize details, such as edges, object boundaries, or cracks. These details represent sharp transitions in intensity value. You can define your own highpass filter with or

CWIMAQVision.NthOrder, or you can use a predefined highpass

filter with

CWIMAQVision.CannyEdgeFilter. CWIMAQVision.EdgeFilter

CWIMAQVision.EdgeFilter or

allows you to find edges in an image using predefined edge detection kernels, such as the Sobel, Prewitt, and Roberts kernels.

Convolution Filter

CWIMAQVision.Convolute allows you to use a predefined set of

lowpass and highpass filters. Each filter is defined by a kernel of coefficients. Use the CWIMAQKernel object to define the filter. Use

CWIMAQKernel.LoadKernel to load a predefined kernel into the

object. If the predefined kernels do not meet your needs, use the

CWIMAQKernel.SetSize method to set the size of the kernel and the CWIMAQKernel.Element property to set the data in the kernel.

Nth Order Filter

CWIMAQVision.NthOrder allows you to define a lowpass or highpass

filter depending on the value of N that you choose. One specific Nth order filter, the median filter, removes speckle noise, which appears as small black and white dots. For more information about Nth order filters, refer to Chapter 5, Image Processing, of the IMAQ Vision Concepts Manual.

CWIMAQVision.Convolute

Grayscale Morphology

Perform grayscale morphology when you want to filter grayscale features of an image. Grayscale morphology helps you remove or enhance isolated features, such as bright pixels on a dark background. Use these transformations on a grayscale image to enhance non-distinct features before thresholding the image in preparation for particle analysis.

Grayscale morphological transformations, which include erosions and dilations, compare a pixel to those pixels that surround it. An erosion keeps the smallest pixel values. A dilation keeps the largest pixel values.

For more information about grayscale morphology transformations, refer to Chapter 5, Image Processing, of the IMAQ Vision Concepts Manual.

IMAQ Vision for Visual Basic User Manual 2-10 ni.com

FFT

Chapter 2 Getting Measurement-Ready Images

Use CWIMAQVision.GrayMorphology to perform one of the following seven transformations:

• Erosion—Reduces the brightness of pixels that are surrounded by neighbors with a lower intensity.

• Dilation—Increases the brightness of pixels surrounded by neighbors with a higher intensity. A dilation has the opposite effect of an erosion.

• Opening—Removes bright pixels isolated in dark regions and smooths boundaries.

• Closing—Removes dark pixels isolated in bright regions and smooths boundaries.

• Proper-opening—Removes bright pixels isolated in dark regions and smooths the inner contours of particles.

• Proper-closing—Removes dark pixels isolated in bright regions and smooths the inner contours of particles.

• Auto-median—Generates simpler particles that have fewer details.

Use the Fast Fourier Transform (FFT) to convert an image into its frequency domain. In an image, details and sharp edges are associated with mid to high spatial frequencies because they introduce significant gray-level variations over short distances. Gradually varying patterns are associated with low spatial frequencies.

An image can have extraneous noise, such as periodic stripes, introduced during the digitization process. In the frequency domain, the periodic pattern is reduced to a limited set of high spatial frequencies. Also, the imaging setup may produce non-uniform lighting of the field of view, which produces an image with a light drift superimposed on the information you want to analyze. In the frequency domain, the light drift appears as a limited set of low frequencies around the average intensity of the image, which is the DC component.

You can use algorithms working in the frequency domain to isolate and remove these unwanted frequencies from the image. Complete the following steps to obtain an image in which the unwanted pattern has disappeared but the overall features remain:

1. Use

CWIMAQVision.FFT to convert an image from the spatial domain

to the frequency domain. This method computes the FFT of the image and results in a complex image representing the frequency information of the image.

Chapter 2 Getting Measurement-Ready Images

2. Improve the image in the frequency domain with a lowpass or highpass frequency filter. Specify which type of filter to use with

CWIMAQVision.CxAttenuate or CWIMAQVision.CxTruncate.

Lowpass filters smooth noise, details, textures, and sharp edges in an image. Highpass filters emphasize details, textures, and sharp edges in images, but they also emphasize noise.

• Lowpass attenuation—The amount of attenuation is directly

proportional to the frequency information. At low frequencies, there is little attenuation. As the frequencies increase, the attenuation increases. This operation preserves all of the zero frequency information. Zero frequency information corresponds to the DC component of the image or the average intensity of the image in the spatial domain.

• Highpass attenuation—The amount of attenuation is inversely

proportional to the frequency information. At high frequencies, there is little attenuation. As the frequencies decrease, the attenuation increases. The zero frequency component is removed entirely.

• Lowpass truncation—Specify a frequency. The frequency

components above the ideal cutoff frequency are removed, and the frequencies below it remain unaltered.

• Highpass truncation—Specify a frequency. The frequency

components above the ideal cutoff frequency remain unaltered, and the frequencies below it are removed.

3. To transform the image back to the spatial domain, use

CWIMAQVision.InverseFFT.

Complex Image Operations

CWIMAQVision.ReplaceComplexPlane and CWIMAQVision.ExtractComplexPlane allow you to access, process,

and update independently the magnitude, phase, real, and imaginary planes of a complex image. You can also convert a complex image to an array and back with

CWIMAQImage.ArrayToImage.

IMAQ Vision for Visual Basic User Manual 2-12 ni.com

CWIMAQImage.ImageToArray and

Making Grayscale and Color Measurements

This chapter describes how to take measurements from grayscale and color images. You can make inspection decisions based on image statistics, such as the mean intensity level in a region. Based on the image statistics, you can perform many machine vision inspection tasks on grayscale or color images, such as detecting the presence or absence of components, detecting flaws in parts, and comparing a color component with a reference. Figure 3-1 illustrates the basic steps involved in making grayscale and color measurements.

Define Regions of Interest

Figure 3-1. Steps to Taking Grayscale and Color Measurements

Define Regions of Interest

An ROI is an area of an image in which you want to focus the image analysis. You can define an ROI interactively, programmatically, or with an image mask.

Defining Regions Interactively

You can interactively define an ROI in a viewer that displays an image. Use the tools from the right-click menu to interactively define and manipulate the ROIs. Table 3-1 describes each of the tools and the manner in which you use them.

Measure

Grayscale Statistics

Measure

Color Statistics

Chapter 3 Making Grayscale and Color Measurements

Too l Na m e Function

None Disable the tools.

Selection Tool Select an ROI in the image and adjust the position

Point Select a pixel in the image.

Line Draw a line in the image.

Rectangle Draw a rectangle or square in the image.

Rotated Rectangle Draw a rotated rectangle in the image.

Table 3-1. Tools Palette Functions

of its control points and contours.

Action: Click the appropriate ROI or control points.

Action: Click the appropriate position.

Action: Click the initial position and click again on the final position.

Action: Click one corner and drag to the opposite corner.

Action: Click one corner and drag to the opposite corner to create the rectangle. Then, click on the lines inside the rectangle and drag to adjust the rotation angle.

Oval Draw an oval or circle in the image.

Action: Click the center position and drag to the appropriate size.

Annulus Draw an annulus in the image.

Action: Click the center position and drag to the appropriate size. Adjust the inner and outer radii, and adjust the start and end angle.

Broken Line Draw a broken line in the image.

Action: Click to place a new vertex and double-click to complete the ROI element.

IMAQ Vision for Visual Basic User Manual 3-2 ni.com

Chapter 3 Making Grayscale and Color Measurements

Table 3-1. Tools Palette Functions (Continued)

Too l Na m e Function

Polygon Draw a polygon in the image.

Action: Click to place a new vertex and double-click to complete the ROI element.

Freeline Draw a freehand line in the image.

Action: Click the initial position, drag to the appropriate shape and release the mouse button to complete the shape.

Free Region Draw a freehand region in the image.

Action: Click the initial position, drag to the appropriate shape and release the mouse button to complete the shape.

Zoom Zoom in or zoom out in an image.

Action: Click the image to zoom in. Hold down <Shift> and click to zoom out.

Pan Pan around an image.

Action: Click an initial position, drag to the appropriate position, and release the mouse button to complete the pan.

Hold down <Shift> when drawing an ROI if you want to constrain the ROI to the horizontal, vertical, or diagonal axes, when possible. Use the selection tool to position an ROI by its control points or vertices. ROIs are context sensitive, meaning that the cursor actions differ depending on the ROI with which you interact. For example, if you move the cursor over the side of a rectangle, the cursor changes to indicate that you can click and drag the side to resize the rectangle. If you want to draw more than one ROI in a window, hold down <Ctrl> while drawing additional ROIs. You also can use

CWIMAQViewer.MaxContours to set the maximum number of

contours the viewer can have in its ROI.

In the status bar of the viewer, you can display tool information about the characteristics of ROIs you draw, as shown in Figure 3-2. Check the Show Tool Info check box on the Status Bar property page during design time, or set the

CWIMAQViewer.ShowToolInfo property to True during run

time to display tool information. You also can show or hide the tool information from the right-click menu.

Chapter 3 Making Grayscale and Color Measurements

2 3 4 5 7

1 Anchoring Coordinates of a Region of Interest 2 Size of the Image 3 Zoom Factor 4 Image Type Indicator (8-bit, 16-bit, Float,

RGB32, RGBU64, HSL, Complex)

5 Pixel Intensity 6 Coordinates of the Mouse 7 Size of an Active Region of Interest 8 Length and Horizontal Angle of a Line Region

Figure 3-2. Tools Information

IMAQ Vision for Visual Basic User Manual 3-4 ni.com

During design time, use the Menu property page to select which tools appear in the right-click menu. You also can designate a default tool from this property page. During run time, set the to select the tools to display, and set default tool.

Defining Regions Programmatically

You can define ROIs programmatically using the CWIMAQRegions collection. In IMAQ Vision, shapes are represented by shape objects. For example, CWIMAQPoint represents a point, and CWIMAQLine represents a line. Use the following methods listed in Table 3-2 to add various shapes to the regions.

Table 3-2. Methods that Add Shapes to Regions

Method Description

AddPoint adds a point to the ROI

AddLine adds a line to the ROI

AddRectangle adds a rectangle to the ROI

Chapter 3 Making Grayscale and Color Measurements

CWIMAQViewer.MenuItems

CWIMAQViewer.Tool to select the

AddRotatedRectangle adds a rotated rectangle to the ROI

AddOval adds an oval to the ROI

AddAnnulus adds an annulus to the ROI

AddBrokenLine adds a broken line to the ROI

AddPolygon adds a polygon to the ROI

AddFreeline adds a free line to the ROI

AddFreeregion adds a free region to the ROI

AddRegion adds a region object to the ROI

Use the

CWIMAQRegions.CopyTo method to copy all the data from one

CWIMAQRegions object to another.

You can define the regions on a viewer and access the regions using the

CWIMAQViewer.Regions property.

The individual CWIMAQRegion objects provide access to the shapes in the collection. Each region has one shape object associated with it. Use the

CWIMAQRegion.Shape property to determine what type of shape the

Chapter 3 Making Grayscale and Color Measurements

CWIMAQRegion contains. When you know the type of shape that the region contains, you can set the region into a shape variable and use that variable to manipulate the shape properties. For example, the following code resizes a rectangle selected on the viewer:

Dim MyRectangle As CWIMAQRectangle

Set MyRectangle = CWIMAQViewer1.Regions(1)

MyRectangle.Width = 100

MyRectangle.Height = 100

You also can pass CWIMAQRegion objects to any IMAQ Vision method that takes a shape as a parameter. However, if the CWIMAQRegion does not contain the type of shape object that the method requires, a type mismatch error results.

Defining Regions with Masks

You can define regions to process with image masks. An image mask is an 8-bit image of the same size as or smaller than the image you want to process. Pixels in the mask image determine if the corresponding pixel in the source image needs to be processed. If a pixel in the image mask has a value other than 0, the corresponding pixel in the source image is processed. If a pixel in the image mask has a value of 0, the corresponding pixel in the source image is left unchanged.

You can use a mask to define particles in a grayscale image when you need to make intensity measurements on those particles. First, threshold the image to make a new binary image. For more information about binary images, refer to Chapter 4, Performing Particle Analysis. You can input the binary image or a labeled version of the binary image as a mask image to the intensity measurement method. If you want to make color comparisons, convert the binary image into a CWIMAQRegions collection using

CWIMAQVision.MaskToRegions.

Measure Grayscale Statistics

You can measure grayscale statistics in images using light meters or quantitative analysis methods. You can obtain the center of energy for an image with the centroid method.

Use

CWMachineVision.LightMeterPoint to measure the

light intensity at a point in the image. Use

CWMachineVision.LightMeterLine to get the pixel value statistics

along a line in the image, such as mean intensity, standard deviation,

IMAQ Vision for Visual Basic User Manual 3-6 ni.com

minimum intensity, and maximum intensity. Use

CWMachineVision.LightMeterRectangle to get the pixel

value statistics within a rectangular region in an image.

Use

CWIMAQVision.Quantify to obtain the following statistics about the

entire image or individual regions in the image: mean intensity, standard deviation, minimum intensity, maximum intensity, area, and the percentage of the image that you analyzed. You can specify regions in the image with a labeled image mask. A labeled image mask is a binary image that has been processed so that each region in the image mask has a unique intensity value. Use

Use

CWIMAQVision.Label2 to label the image mask.

CWIMAQVision.Centroid2 to compute the energy center of the

image, or of a region within an image.

Measure Color Statistics

Most image processing and analysis methods apply to 8-bit and 16-bit images. However, you can analyze and process individual components of a color image.

Using

CWIMAQVision.ExtractColorPlanes, you can break down

a color image into various sets of primary components, such as RGB (Red, Green, and Blue), HSI (Hue, Saturation, and Intensity), HSL (Hue, Saturation, and Luminance), or HSV (Hue, Saturation, and Value). Each component becomes an 8-bit or 16-bit image that you can process like any other grayscale image. Use

CWIMAQVision.ExtractSingleColorPlane to extract a single color

plane from an image. Use reassemble a color image from a set of three 8-bit or 16-bit images, where each image becomes one of the three primary components. Figures 3-3 and 3-4 illustrate how a color image breaks down into its three components.

Chapter 3 Making Grayscale and Color Measurements

CWIMAQVision.ReplaceColorPlanes to

Chapter 3 Making Grayscale and Color Measurements

Color

Image

Red

Green

Blue

Hue

Saturation

Intensity

Hue

Saturation

Luminance

Hue

Saturation

Value

8-bit Image Processing

Red

Green

Blue

Hue

Saturation

Intensity

Hue

Saturation

Luminance

Hue

Saturation

Value

Color

Image

Figure 3-3. Primary Components of an 32-bit Color Image

Color

Image

1664

Red

Green

Blue

161616

16-bit

Image

Processing

16161616

Red

Green

Blue

1664

Color

Image

Figure 3-4. Primary Components of a 64-bit Color Image

A color pixel encoded as a Long value can be decomposed into its individual components using

CWIMAQVision.IntegerToColorValue.

You can convert a pixel value represented in any color model into its components in any other color model using

CWIMAQVision.ColorValueConversion2.

IMAQ Vision for Visual Basic User Manual 3-8 ni.com

Comparing Colors

Chapter 3 Making Grayscale and Color Measurements

You can use the color matching capability of IMAQ Vision to compare or evaluate the color content of an image or regions in an image.

Complete the following steps to compare colors using color matching:

1. Select an image containing the color information that you want to use as a reference. The color information can consist of a single color or multiple dissimilar colors, such as red and blue.

2. Use the entire image or regions in the image to learn the color information using results of the operation in a CWIMAQColorInformation object that you supply as a parameter. The color information object has a color spectrum that contains a compact description of the color information that you learned. Refer to Chapter 14, Color Inspection, of the IMAQ Vision Concepts Manual for more information. Use the CWIMAQColorInformation object to represent the learned color information for all subsequent matching operations.

3. Define an entire image, a region, or multiple regions in an image as the inspection or comparison area.

4. Use

5. Use the color matching score as a measure of similarity between the

CWIMAQVision.MatchColor to compare the learned color

information to the color information in the inspection regions. This method returns an array of scores that indicates how close the matches are to the learned color information.

reference color information and the color information in the image regions being compared.

CWIMAQVision.LearnColor, which stores the

Learning Color Information

When learning color information, choose the color information carefully:

• Specify an image or regions in an image that contain the color or color set that you want to learn.

• Specify the granularity required to represent the color information.

• Choose colors that you want to ignore during matching.

Chapter 3 Making Grayscale and Color Measurements

Specifying the Color Information to Learn

Because color matching only uses color information to measure similarity, the image or regions in the image representing the object should contain only the significant colors that represent the object, as shown in Figure 3-5a. Figure 3-5b illustrates an unacceptable region containing background colors.

Figure 3-5. Template Color Information

The following sections specify when to learn the color information associated with an entire image, a region in an image, or multiple regions in an image.

a. b.

Using the Entire Image

You can use an entire image to learn the color spectrum that represents the entire color distribution of the image. In a fabric identification application, for example, an entire image can specify the color information associated with a certain fabric type, as shown in Figure 3-6.

Figure 3-6. Using the Entire Image to Learn Color Distribution

IMAQ Vision for Visual Basic User Manual 3-10 ni.com

Chapter 3 Making Grayscale and Color Measurements

Using a Region in the Image

You can select a region in the image to provide the color information for comparison. A region is helpful for pulling out the useful color information in an image. Figure 3-7 shows an example of using a region that contains the color information that is important for the application.

Figure 3-7. Using a Single Region to Learn Color Distribution

Using Multiple Regions in the Image

The interaction of light with the object surface creates the observed color of that object. The color of a surface depends on the directions of illumination and the direction from which the surface is observed. Two identical objects may have different appearances because of a difference in positioning or a change in the lighting conditions.

Figure 3-8 shows how light reflects differently off of the 3D surfaces of the fuses, resulting in slightly different colors for identical fuses. To view the color differences, compare the 3-amp fuse in the upper row with the 3-amp fuse in the lower row.

If you learn the color spectrum by drawing a region of interest around the 3-amp fuse in the upper row, and then do a color matching for the 3-amp fuse in the upper row, you get a very high match score for it—close to 1000. The match score for the 3-amp fuse in the lower row is low—around 500. This problem could cause a mismatch for the color matching in a fuse box inspection process.

The color learning functionality of IMAQ Vision uses a clustering process to find the representative colors from the color information specified by one or multiple regions in the image. To create a representative color spectrum for all 3-amp fuses in the learning phase, draw a Region around the 3-amp fuse in the upper row, hold down <Ctrl>, and draw another Region around the 3 amp fuse in the lower row. The new color spectrum represents 3-amp

Chapter 3 Making Grayscale and Color Measurements

fuses much better and results in high match scores—around 800—for both the fuses. You can use an unlimited number of samples to learn the representative color spectrum for a specified template.

1 Regions used to learn color information

Figure 3-8. Using Multiple Regions to Learn Color Distribution

Choosing a Color Representation Sensitivity

When you learn a color, you need to specify the granularity required to specify the color information. An image that contains a few well-separated colors in the color space requires a lower granularity to describe the color than an image that contains colors that are close to one another in the color space. Use the

CWIMAQVision.LearnColor to specify the granularity you want to

use to represent the colors. For more information about color sensitivity, refer to the Color Sensitivity section of Chapter 5, Performing Machine

Vision Tasks.

IMAQ Vision for Visual Basic User Manual 3-12 ni.com

ColorSensitivity parameter of

Chapter 3 Making Grayscale and Color Measurements

Ignoring Learned Colors

You can ignore certain color components in color matching by setting the corresponding component in the input color spectrum array to –1. To set a particular color component, follow these steps:

1. Copy

2. Set the corresponding components of the array.

3. Assign this array to

For example, setting the last component in the color spectrum to –1 ignores the color white. Setting the second to last component in the color spectrum array to –1 ignores the color black. To ignore other color components in color matching, determine the index to the color spectrum by locating the corresponding bins in the color wheel, where each bin corresponds to a component in the color spectrum array. Ignoring certain colors such as the background color results in a more accurate color matching score. Ignoring the background color also provides more flexibility when defining the regions of interest in the color matching process. Ignoring certain colors, such as the white color created by glare on a metallic surface, also improves the accuracy of the color matching. Experiment learning the color information about different parts of the images to determine which colors to ignore. For more information about the color wheel and color bins, refer to Chapter 14, Color Inspection, in the IMAQ Vision Concepts Manual.

CWIMAQColorInformation.ColorSpectrum, or create your

own array.

CWIMAQColorInformation.ColorSpectrum

on the CWIMAQColorInformation object you want to use as input during the match phase.

Performing Particle Analysis

This chapter describes how to perform particle analysis on the images. Use particle analysis to find statistical information about particles, such as the presence, size, number, and location of particle regions. With this information, you can perform many machine vision inspection tasks, such as detecting flaws on silicon wafers or detecting soldering defects on electronic boards. Examples of how particle analysis can help you perform web inspection tasks include locating structural defects on wood planks or detecting cracks on plastic sheets.

Figure 4-1 illustrates the steps involved in performing particle analysis.

Create a Binary Image

Improve a Binary Image

Make Particle Measurements

in Pixels or Real-World Units

Figure 4-1. Steps for Performing Particle Analysis

Create a Binary Image

Threshold the grayscale or color image to create a binary image. Creating a binary image separates the objects that you want to inspect from the background. The threshold operation sets the background pixels to 0 in the binary image, while setting the object pixels to a non-zero value. Object pixels have a value of 1 by default, but you can set the object pixels to any value or retain their original value.

You can use different techniques to threshold the image. If all the objects of interest in the grayscale image fall within a continuous range of intensities and you can specify this threshold range manually, use

CWIMAQVision.Threshold to threshold the image.

Chapter 4 Performing Particle Analysis

If all the objects in the grayscale image are either brighter or darker than the background, you can use automatically determine the optimal threshold range and threshold the image. Automatic thresholding techniques offer more flexibility than simple thresholds based on fixed ranges. Because automatic thresholding techniques determine the threshold level according to the image histogram, the operation is more independent of changes in the overall brightness and contrast of the image than a fixed threshold. These techniques are more resistant to changes in lighting, which makes them well suited for automated inspection tasks.

If the grayscale image contains objects that have multiple discontinuous grayscale values, use multiple threshold ranges.

If you need to threshold a color image, use

CWIMAQVision.ColorThreshold. You must specify threshold

ranges for each of the color planes—Red, Green, and Blue; or Hue, Saturation, and Luminance. The binary image resulting from a color threshold is an 8-bit binary image.

Improve the Binary Image

CWIMAQVision.AutoThreshold to

CWIMAQVision.MultiThreshold2 to specify

After you threshold the image, you may want to improve the resulting binary image with binary morphology. You can use primary binary morphology or advanced binary morphology to remove unwanted particles, separate connected particles, or improve the shape of particles. Primary morphology methods work on the image as a whole by processing pixels individually. Advanced morphology operations are built upon the primary morphological operators and work on particles as opposed to pixels.

The advanced morphology methods that improve binary images require that you specify the type of connectivity to use. Connectivity specifies how IMAQ Vision determines if two adjacent pixels belong to the same particle. If you have a particle that contains narrow areas, use connectivity-8 to ensure that the software recognizes the connected pixels as one particle. If you have two particles that touch at one point, use connectivity-4 to ensure that the software recognizes the pixels as two separate particles. For more information about connectivity, refer to Chapter 9, Binary Morphology, of the IMAQ Vision Concepts Manual.

Note Use the same type of connectivity throughout the application.

IMAQ Vision for Visual Basic User Manual 4-2 ni.com

Removing Unwanted Particles

Use CWIMAQVision.RejectBorder to remove particles that touch the border of the image. Reject particles on the border of the image when you suspect that the information about those particles is incomplete.

Use

CWIMAQVision.RemoveParticle to remove large or small particles

that do not interest you. You also can use the methods in Unlike size and shape of the remaining particles.

CWIMAQVision.Morphology to remove small particles.

CWIMAQVision.RemoveParticle, these three methods alter the

Chapter 4 Performing Particle Analysis

Erode, Open, and POpen

Use the hit-miss method of particular configurations of pixels, which you define with a structuring element. Depending on the configuration of the structuring element, the hit-miss method can locate single isolated pixels, cross-shape or longitudinal patterns, right angles along the edges of particles, and other user-specified shapes. For more information about structuring elements, refer to Chapter 9, Binary Morphology, of the IMAQ Vision Concepts Manual.

If you know enough about the shape features of the particles you want to keep, use

CWIMAQVision.ParticleFilter2 to filter out particles that

do not interest you. If you do not have enough information about the particles you want to keep at this point in the processing, use the particle measurement methods to obtain this information before applying a particle filter. Refer to the Make Particle Measurements section for more information about the measurement methods.

Separating Touching Particles

Use CWIMAQVision.Separation or apply an erosion or an open operation with objects. separates particles without modifying their shapes. However, erosion and open operations alter the shape of all the particle.

CWIMAQVision.Separation is an advanced operation that

CWIMAQVision.Morphology to locate

CWIMAQVision.Morphology to separate touching

Note A separation is a time-intensive operation compared to an erosion or open operation.

Consider using an erosion if speed is an issue with the application.

Chapter 4 Performing Particle Analysis

Improving Particle Shapes

Use CWIMAQVision.FillHole to fill holes in the particles. Use

CWIMAQVision.Morphology to perform a variety of operations on the

particles. You can use the Open, Close, Proper Open, Proper Close, and auto-median operations to smooth the boundaries of the particles. Open and Proper Open Smooth the boundaries of the particle by removing small isthmuses, while close widens the isthmuses. Close and Proper Close fill small holes in the particle. Auto-median removes isthmuses and fills holes. For more information about these operations, refer to Chapter 9, Binary Morphology, in the IMAQ Vision Concepts Manual.

Make Particle Measurements

After you create a binary image and improve it, you can make particle measurements. With these measurements you can determine the location of particles and their shape features. Use the following methods to perform particle measurements:

•

CWIMAQVision.ParticleReport—This method returns a

CWIMAQParticleReport object, which contains, for each particle, nine of the most commonly used measurements, including the particle area, bounding rectangle, and center of mass. The bounding rectangle is returned as one measurement, but contains four measurement elements. The center of mass is returned as one measurement, but contains two elements.

•

CWIMAQVision.ParticleMeasurement—This method takes the

measurement you want to apply to all particles, and returns an array that contains the specified measurement for each particle.

Table 4-1 lists all of the measurements that

CWIMAQVision.ParticleMeasurement returns.

Table 4-1. Measurement Types

Measurement Description

cwimaqMeasurementArea Area of the particle.

cwimaqMeasurementAreaByImageArea Percentage of the particle Area covering

the Image Area.

cwimaqMeasurementAreaByParticleAndHolesArea Percentage of the particle Area in

relation to its Particle & Holes’ Area.

IMAQ Vision for Visual Basic User Manual 4-4 ni.com

Chapter 4 Performing Particle Analysis

Table 4-1. Measurement Types (Continued)

Measurement Description

cwimaqMeasurementAverageHorizSegmentLength Average length of a horizontal segment

in the particle.

cwimaqMeasurementAverageVertSegmentLength Average length of a vertical segment in

the particle.

cwimaqMeasurementBoundingRectBottom Y-coordinate of the lowest particle point.

cwimaqMeasurementBoundingRectDiagonal Distance between opposite corners of

the bounding rectangle.

cwimaqMeasurementBoundingRectHeight Distance between the Y-coordinate

of highest particle point and the Y-coordinate of the lowest particle point.

cwimaqMeasurementBoundingRectLeft X-coordinate of the leftmost particle

point.

cwimaqMeasurementBoundingRectRight X-coordinate of the rightmost particle

point.

cwimaqMeasurementBoundingRectTop Y-coordinate of highest particle point.

cwimaqMeasurementBoundingRectWidth Distance between the X-coordinate

of the leftmost particle point and the X-coordinate of the rightmost particle point.

cwimaqMeasurementCenterMassX X-coordinate of the point representing

the average position of the total particle mass assuming every point in the particle has a constant density.

cwimaqMeasurementCenterMassY Y-coordinate of the point representing

the average position of the total particle mass assuming every point in the particle has a constant density.

cwimaqMeasurementCompactnessFactor Area divided by the product of

Bounding Rect Width and Bounding Rect Height.

cwimaqMeasurementConvexHullArea Area of the smallest convex polygon

containing all points in the particle.

Chapter 4 Performing Particle Analysis

Table 4-1. Measurement Types (Continued)

Measurement Description

cwimaqMeasurementConvexHullPerimeter Perimeter of the smallest convex

polygon containing all points in the particle.

cwimaqMeasurementElongationFactor Max Feret Diameter divided by

Equivalent Rect Short Side (Feret).

cwimaqMeasurementEquivalentEllipseMajorAxis Length of the major axis of the ellipse

with the same perimeter and area as the particle.

cwimaqMeasurementEquivalentEllipseMinorAxis Length of the minor axis of the ellipse

with the same perimeter and area as the particle.

cwimaqMeasurementEquivalentEllipseMinorAxisFeret Length of the minor axis of the ellipse

with the same area as the particle, and Major Axis equal in length to the Max Feret Diameter.

cwimaqMeasurementEquivalentRectDiagonal Distance between opposite corners of

the rectangle with the same perimeter and area as the particle.

cwimaqMeasurementEquivalentRectLongSide Longest side of the rectangle with the

same perimeter and area as the particle.

cwimaqMeasurementEquivalentRectShortSide Shortest side of the rectangle with the

same perimeter and area as the particle.

cwimaqMeasurementEquivalentRectShortSideFeret Shortest side of the rectangle with the

same area as the particle, and longest side equal in length to the Max Feret Diameter.

cwimaqMeasurementFirstPixelX X-coordinate of the highest, leftmost

particle pixel.

cwimaqMeasurementFirstPixelY Y-coordinate of the highest, leftmost

particle pixel.

cwimaqMeasurementHeywoodCircularityFactor Perimeter divided by the circumference

of a circle with the same area.

IMAQ Vision for Visual Basic User Manual 4-6 ni.com

Chapter 4 Performing Particle Analysis

Table 4-1. Measurement Types (Continued)

Measurement Description

cwimaqMeasurementHolesArea Sum of the areas of each hole in the

particle.

cwimaqMeasurementHolesPerimeter Sum of the perimeters of each hole in the

particle.

cwimaqMeasurementHuMoment1 The first Hu moment.

cwimaqMeasurementHuMoment2 The second Hu moment.

cwimaqMeasurementHuMoment3 The third Hu moment.

cwimaqMeasurementHuMoment4 The fourth Hu moment.

cwimaqMeasurementHuMoment5 The fifth Hu moment.

cwimaqMeasurementHuMoment6 The sixth Hu moment.

cwimaqMeasurementHuMoment7 The seventh Hu moment.

cwimaqMeasurementHydraulicRadius The particle area divided by the particle

perimeter.

cwimaqMeasurementImageArea Area of the image.

cwimaqMeasurementMaxFeretDiameter Distance between the start and end of the

line segment connecting the two perimeter points that are the furthest apart.

cwimaqMeasurementMaxFeretDiameterEndX X-coordinate of the end of the line

segment connecting the two perimeter points that are the furthest apart.

cwimaqMeasurementMaxFeretDiameterEndY Y-coordinate of the end of the line

segment connecting the two perimeter points that are the furthest apart.

cwimaqMeasurementMaxFeretDiameterOrientation The angle of the line segment

connecting the two perimeter points that are the furthest apart.

cwimaqMeasurementMaxFeretDiameterStartX X-coordinate of the start of the line

segment connecting the two perimeter points that are the furthest apart.

Chapter 4 Performing Particle Analysis

Table 4-1. Measurement Types (Continued)

Measurement Description

cwimaqMeasurementMaxFeretDiameterStartY Y-coordinate of the start of the line

segment connecting the two perimeter points that are the furthest apart.

cwimaqMeasurementMaxHorizSegmentLengthLeft X-coordinate of the leftmost pixel in the

longest row of contiguous pixels in the particle.

cwimaqMeasurementMaxHorizSegmentLengthRight X-coordinate of the rightmost pixel in

the longest row of contiguous pixels in the particle.

cwimaqMeasurementMaxHorizSegmentLengthRow Y-coordinate of all of the pixels in the

longest row of contiguous pixels in the particle.

cwimaqMeasurementMomentOfInertiaXX The moment of inertia in the X direction

twice.

cwimaqMeasurementMomentOfInertiaXXX The moment of inertia in the X direction

three times.

cwimaqMeasurementMomentOfInertiaXXY The moment of inertia in the X direction

twice and the Y direction once.

cwimaqMeasurementMomentOfInertiaXY The moment of inertia in the X and Y

directions.

cwimaqMeasurementMomentOfInertiaXYY The moment of inertia in the X direction

once and the Y direction twice.

cwimaqMeasurementMomentOfInertiaYY The moment of inertia in the Y direction

twice.

cwimaqMeasurementMomentOfInertiaYYY The moment of inertia in the Y direction

three times.

cwimaqMeasurementNormMomentOfInertiaXX The normalized moment of inertia in the

X direction twice.

cwimaqMeasurementNormMomentOfInertiaXXX The normalized moment of inertia in the

X direction three times.

cwimaqMeasurementNormMomentOfInertiaXXY The normalized moment of inertia in the

X direction twice and the Y direction once.

IMAQ Vision for Visual Basic User Manual 4-8 ni.com

Chapter 4 Performing Particle Analysis

Table 4-1. Measurement Types (Continued)

Measurement Description

cwimaqMeasurementNormMomentOfInertiaXY The normalized moment of inertia in the

X and Y directions.

cwimaqMeasurementNormMomentOfInertiaXYY The normalized moment of inertia in the

X direction once and the Y direction twice.

cwimaqMeasurementNormMomentOfInertiaYY The normalized moment of inertia in the

Y direction twice.

cwimaqMeasurementNormMomentOfInertiaYYY The normalized moment of inertia in the

Y direction three times.

cwimaqMeasurementNumberOfHoles Number of holes in the particle.

cwimaqMeasurementNumberOfHorizSegments Number of horizontal segments in the

particle.

cwimaqMeasurementNumberOfVertSegments Number of vertical segments in the

particle.

cwimaqMeasurementOrientation The angle of the line that passes through

the particle Center of Mass about which the particle has the lowest moment of inertia.

cwimaqMeasurementParticleAndHolesArea Percentage of the particle Area in

relation to its Particle & Holes’ Area.

cwimaqMeasurementPerimeter Sum of the perimeters of each hole in the

particle.

cwimaqMeasurementRatioOfEquivalentEllipseAxes Equivalent Ellipse Major Axis divided

by Equivalent Ellipse Minor Axis.

cwimaqMeasurementRatioOfEquivalentRectSides Equivalent Rect Long Side divided by

Equivalent Rect Short Side.

cwimaqMeasurementSumX The sum of all X-coordinates in the

particle.

cwimaqMeasurementSumXX The sum of all X-coordinates squared in

the particle.

cwimaqMeasurementSumXXX The sum of all X-coordinates cubed in

the particle.

Chapter 4 Performing Particle Analysis

Table 4-1. Measurement Types (Continued)

Measurement Description

cwimaqMeasurementSumXXY The sum of all X-coordinates squared

times Y-coordinates in the particle.

cwimaqMeasurementSumXY The sum of all X-coordinates times

Y-coordinates in the particle.

cwimaqMeasurementSumXYY The sum of all X-coordinates times

Y-coordinates squared in the particle.

cwimaqMeasurementSumY The sum of all Y-coordinates in the

particle.

cwimaqMeasurementSumYY The sum of all Y-coordinates squared in

the particle.

cwimaqMeasurementSumYYY The sum of all Y-coordinates cubed in

the particle.

cwimaqMeasurementTypesFactor Factor relating area to moment of

inertia.

cwimaqMeasurementWaddelDiskDiameter Diameter of a disk with the same area as

the particle.

IMAQ Vision for Visual Basic User Manual 4-10 ni.com

Performing Machine Vision Tasks

This chapter describes how to perform many common machine vision inspection tasks. The most common inspection tasks are detecting the presence or absence of parts in an image and measuring the dimensions of parts to see if they meet specifications.

Measurements are based on characteristic features of the object represented in the image. Image processing algorithms traditionally classify the type of information contained in an image as edges, surfaces and textures, or patterns. Different types of machine vision algorithms leverage and extract one or more types of information.

Edge detectors and derivative techniques—such as rakes, concentric rakes, and spokes—use edges represented in the image. They locate, with high accuracy, the position of the edge of an object in the image. For example, you can a technique called clamping, which uses the edge location to measure the width of the part. You can combine multiple edge locations to compute intersection points, projections, circles, or ellipse fits.

Pattern matching algorithms use edges and patterns. Pattern matching can locate with very high accuracy the position of fiducials or characteristic features of the part under inspection. Those locations can then be combined to compute lengths, angles, and other object measurements.

The robustness of the measurement relies on the stability of the image acquisition conditions. Sensor resolution, lighting, optics, vibration control, part fixture, and general environment are key components of the imaging setup. All the elements of the image acquisition chain directly affect the accuracy of the measurements.

Figure 5-1 illustrates the basic steps involved in performing machine vision.

Chapter 5 Performing Machine Vision Tasks

Locate Objects to Inspect

Set Search Areas

Find Measurement Points

Convert Pixel Coordinates to

Real-World Coordinates

Figure 5-1. Steps to Performing Machine Vision

Note

Diagram items enclosed with dashed lines are optional steps.

Locate Objects to Inspect

In a typical machine vision application, you extract measurements from regions of interest rather than the entire image. To use this technique, the parts of the object you are interested in must always appear inside the regions of interest you define.

If the object under inspection is always at the same location and orientation in the images you need to process, defining regions of interest is simple. Refer to the Set Search Areas section of this chapter for information about selecting a region of interest.

Make Measurements

Identify Parts Under Inspection

Classify Objects

Display Results

Read

Characters

Read

Symbologies

Often, the object under inspection appears rotated or shifted in the image you need to process with respect to the reference image in which you located the object. When this occurs, the ROIs must shift and rotate with the parts of the object in which you are interested. For the ROIs to move with the object, you must define a reference coordinate system relative to the object in the reference image. During the measurement process, the coordinate system moves with the object when it appears shifted and rotated in the image you need to process. This coordinate system is referred

IMAQ Vision for Visual Basic User Manual 5-2 ni.com

Chapter 5 Performing Machine Vision Tasks

to as the measurement coordinate system. The measurement methods automatically move the ROIs to the correct position using the position of the measurement coordinate system with respect to the reference coordinate system. For information about coordinate systems, refer to Chapter 13, Dimensional Measurements, of the IMAQ Vision Concepts Manual.

You can build a coordinate transformation using edge detection or pattern matching. The output of the edge detection and pattern matching methods that build a coordinate transformation is a CWMVCoordinateTransformation object, which contains a reference coordinate system and a measurement coordinate system. Some machine vision methods take this transformation and adjust the regions of inspection automatically. You also can use these outputs to move the regions of inspection relative to the object programmatically.

Using Edge Detection to Build a Coordinate Transformation

You can build a coordinate transformation using two edge detection techniques. Use to define a reference coordinate system using one rectangular region. Use

CWMachineVision.FindCoordTransformUsingTwoRects to define a

reference coordinate system using two independent rectangular regions. Follow the steps below to build a coordinate transformation using edge detection.

CWMachineVision.FindCoordTransformUsingRect

Note To use this technique, the object cannot rotate more than ±65° in the image.

1. Specify one or two rectangular ROIs.

a. If you use

CWMachineVision.FindCoordTransformUsingRect,

specify one rectangular ROI that includes part of two straight, nonparallel boundaries of the object, as shown in Figure 5-2. This rectangular region must be large enough to include these boundaries in all the images you want to inspect.

Chapter 5 Performing Machine Vision Tasks

1 Search Area for the Coordinate System 2 Object Edges

a. b.

Figure 5-2. Coordinate Systems of a Reference Image and Inspection Image

b. If you use

CWMachineVision.FindCoordTransformUsingTwoRects,

specify two rectangular ROIs, each containing one separate, straight boundary of the object, as shown in Figure 5-3. The boundaries cannot be parallel. The regions must be large enough to include the boundaries in all of the images you want to inspect.

3 Origin of the Coordinate System 4 Measurement Area

IMAQ Vision for Visual Basic User Manual 5-4 ni.com

Chapter 5 Performing Machine Vision Tasks

1 Primary Search Area 2 Secondary Search Area

3 Origin of the Coordinate System 4 Measurement Area

Figure 5-3. Locating Coordinate System Axes with Two Search Areas

2. Choose the parameters you need to locate the edges on the object.

3. Choose the coordinate system axis direction.

4. Choose the results that you want to overlay onto the image.

5. Choose the mode for the method. To build a coordinate transformation for the first time, set the

FirstRun parameter to True. To update the

coordinate transformation in subsequent images, set this parameter to

False.

Using Pattern Matching to Build a Coordinate Transformation

You can build a coordinate transformation using pattern matching. Use

CWMachineVision.FindCoordTransformUsingPattern to define a

reference coordinate system based on the location of a reference feature. Use this technique when the object under inspection does not have straight, distinct edges. Follow the steps below to build a coordinate transformation using pattern matching.

Note The object may rotate 360° in the image using this technique if you use

rotation-invariant pattern matching.

Chapter 5 Performing Machine Vision Tasks

1. Define a template that represents the part of the object that you want to use as a reference feature. For more information about defining a template, refer to the Find Measurement Points section.

2. Define a rectangular search area in which you expect to find the template.

3. Set the CWMVFindCTUsingPatternOptions object to

cwimaqRotationInvariant when you expect the template

to appear rotated in the inspection images. Otherwise, set it to

cwimaqShiftInvariant.

4. Choose the results you want to overlay onto the image.

5. Choose the mode for the method. To build a transformation for the first time, set the transformation in subsequent images, set this parameter to

MatchMode property of the

FirstRun parameter to True. To update the

False.

IMAQ Vision for Visual Basic User Manual 5-6 ni.com

Chapter 5 Performing Machine Vision Tasks

Choosing a Method to Build the Coordinate Transformation

Figure 5-4 guides you through choosing the best method for building a coordinate transformation for the application.

Start

Object positioning

accuracy better

than ±65 degrees.

Ye s

The object under inspection has a straight, distinct edge (main axis).

Ye s

The object contains a

second distinct edge not parallel

to the main axis in the same

search area.

Ye s

Build a coordinate transformation based on edge detection

using a single search area.

The object contains

a second distinct edge not

parallel to the main axis in a

separate search area.

Ye s

Build a coordinate transformation based on edge detection using two

distinct search areas.

Object positioning

accuracy better

than ±5 degrees.

Ye s

Build a coordinate

transformation based on

pattern matching

shift invariant strategy.

Build a coordinate

transformation based on

pattern matching

rotation invariant strategy.

End

Figure 5-4. Building a Coordinate Transformation

Chapter 5 Performing Machine Vision Tasks

Set Search Areas

Select ROIs in the images to limit the areas in which you perform the processing and inspection. You can define ROIs interactively or programmatically.

Defining Regions Interactively

Follow these steps to interactively define an ROI:

1. Call

CWMachineVision.SetupViewerFor<shapename>Selection.

The following

Point, Rectangle, and RotatedRect. This method configures the

viewer to display the appropriate tools for the shape you want to select.

2. Draw an ROI on the image. Resize or reposition the ROI until it defines the area of the image you want to process.

3. Use

You also can use the techniques described in Chapter 3, Making Grayscale

and Color Measurements, to select an ROI.

CWMachineVision.GetSelected<shapename>FromViewer

to programmatically retrieve the shape from the viewer.

<shapename> values are available: Annulus, Line,

Table 5-1 indicates which ROI selection methods to use with a given CWMachineVision method.

Table 5-1. ROI Selection Methods to Use with CWMachineVision Methods

CWMachineVision ROI Selection Methods CWMachineVision Method

SetupViewerForRotatedRectSelection

GetSelectedRotatedRectFromViewer

SetupViewerForAnnulusSelection

GetSelectedAnnulusFromViewer

IMAQ Vision for Visual Basic User Manual 5-8 ni.com

FindPattern

MeasureMaximumDistance

MeasureMinimumDistance

FindStraightEdge

LightMeterRectangle

FindCircularEdge

FindConcentricEdge

Chapter 5 Performing Machine Vision Tasks

Table 5-1. ROI Selection Methods to Use with CWMachineVision Methods (Continued)

CWMachineVision ROI Selection Methods CWMachineVision Method

SetupViewerForPointSelection

GetSelectedPointFromViewer

SetupViewerForLineSelection

GetSelectedLineFromViewer

Defining Regions Programmatically

When you have an automated application, you need to define regions of interest programmatically. You can programmatically define regions by providing basic parameters that describe the region you want to define. You can specify a rotated rectangle by creating a CWIMAQRotatedRectangle object and setting the coordinates of the center, width, height, and rotation angle. You can specify an annulus by creating a CWIMAQAnnulus object and setting the coordinates of the center, inner radius, outer radius, start angle, and end angle. You can specify a point by setting its x-coordinates and y-coordinates. You can specify a line by setting the coordinates of the start and end points.

Refer to Chapter 3, Making Grayscale and Color Measurements, for more information about defining regions of interest.

Find Measurement Points

LightMeterPoint

LightMeterLine

After you set regions of inspection, locate points in the regions on which you can base measurements. You can locate measurement points using edge detection, pattern matching, color pattern matching, and color location.

Finding Features Using Edge Detection

Use the edge detection tools to identify and locate sharp discontinuities in an image. Discontinuities typically represent abrupt changes in pixel intensity values, which characterize the boundaries of objects.

Chapter 5 Performing Machine Vision Tasks

Finding Lines or Circles

If you want to find points along the edge of an object and find a line describing the edge, use and

CWMachineVision.FindConcentricEdge.

CWMachineVision.FindStraightEdge finds edges based

on rectangular search areas, as shown in Figure 5-5.

CWMachineVision.FindConcentricEdge finds edges based

on annular search areas.

CWMachineVision.FindStraightEdge

1 Search Region 2 Search Lines

3 Detected Edge Points 4 Line Fit to Edge Points

Figure 5-5. Finding a Straight Feature

If you want to find points along a circular edge and find the circle that best fits the edge, as shown in Figure 5-6, use

CWMachineVision.FindCircularEdge.

IMAQ Vision for Visual Basic User Manual 5-10 ni.com

Chapter 5 Performing Machine Vision Tasks

1 Annular Search Region 2 Search Lines

Figure 5-6. Finding a Circular Feature

3 Detected Edge Points 4 Circle Fit To Edge Points

These methods locate the intersection points between a set of search lines in the search region and the edge of an object. Specify the separation between the lines that the methods use to detect edges. The methods determine the intersection points based on their contrast, width, and steepness. The software calculates a best-fit line with outliers rejected or a best-fit circle through the points it found. The methods return the coordinates of the edges found.

Finding Edge Points Along One Search Contour

Use CWIMAQVision.SimpleEdge and CWIMAQVision.FindEdges2 to find edge points along a contour. You can find the first edge, last edge, or all edges along the contour. Use image contains little noise and the object and background are clearly differentiated. Otherwise, use

These methods require you to input the coordinates of the points along the search contour. Use

CWIMAQVision.RegionsProfile to obtain the

coordinates from a CWIMAQRegions object that describes the contour. If you have a straight line, use obtain the points along the line instead of using regions.

CWIMAQVision.SimpleEdge when the

CWIMAQVision.FindEdges2.

CWIMAQVision.GetPointsOnLine to

These methods determine the edge points based on their contrast and slope. You can specify if you want to find the edge points using subpixel accuracy.

Chapter 5 Performing Machine Vision Tasks

Finding Edge Points Along Multiple Search Contours

Use the CWIMAQVision.Rake, CWIMAQVision.Spoke, and

CWIMAQVision.ConcentricRake methods to find edge points

along multiple search contours. These methods behave like

CWIMAQVision.FindEdges2, but they find edges on multiple contours.

These methods find only the first edge that meets the criteria along each contour. Pass in a CWIMAQRegions object to define the search region for these methods.

CWIMAQVision.Rake works on a rectangular search region. The search

lines are drawn parallel to the orientation of the rectangle. Control the number of search lines in the region by specifying the distance, in pixels, between each line. Specify the search direction as left to right or right to left for a horizontally oriented rectangle. Specify the search direction as top to bottom or bottom to top for a vertically oriented rectangle.

CWIMAQVision.Spoke works on an annular search region, scanning the

search lines that are drawn from the center of the region to the outer boundary and that fall within the search area. Control the number of lines in the region by specifying the angle, in degrees, between each line. Specify the search direction as either going from the center outward or from the outer boundary to the center.

CWIMAQVision.ConcentricRake works on an annular search region.

The concentric rake is an adaptation of the rake to an annular region. Edge detection is performed along search lines that occur in the search region and that are concentric to the outer circular boundary. Control the number of concentric search lines that are used for the edge detection by specifying the radial distance between the concentric lines in pixels. Specify the direction of the search as either clockwise or counterclockwise.

Finding Points Using Pattern Matching

The pattern matching algorithms in IMAQ Vision measure the similarity between an idealized representation of a feature, called a template, and the feature that may be present in an image. A feature is defined as a specific pattern of pixels in an image. Pattern matching returns the location of the center of the template and the template orientation. Follow these generalized steps to find features in an image using pattern matching:

1. Define a reference or fiducial pattern in the form of a template image.

2. Use the reference pattern to train the pattern matching algorithm with

CWIMAQVision.LearnPattern2.

IMAQ Vision for Visual Basic User Manual 5-12 ni.com

Chapter 5 Performing Machine Vision Tasks

3. Define an image or an area of an image as the search area. A small search area reduces the time to find the features.

4. Set the tolerances and parameters to specify how the algorithm operates at run time using CWIMAQMatchPatternOptions.

5. Test the search algorithm on test images using

CWIMAQVision.MatchPattern2.

6. Verify the results using a ranking method.

Defining and Creating Effective Template Images

The selection of a effective template image plays a critical part in obtaining good results. Because the template image represents the pattern that you want to find, make sure that all the important and unique characteristics of the pattern are well defined in the image.

Several factors are critical in creating a template image. These critical factors include symmetry, feature detail, positional information, and background information.

Symmetry

A rotationally symmetric template is less sensitive to changes in rotation than one that is rotationally asymmetric. A rotationally symmetric template provides good positioning information but no orientation information.

a. b.

a Rotationally Symmetric b Rotationally Asymmetric

Figure 5-7. Symmetry

Chapter 5 Performing Machine Vision Tasks

Feature Detail

A template with relatively coarse features is less sensitive to variations in size and rotation than a model with fine features. However, the model must contain enough detail to identify it.

a Good Feature Detail b Ambiguous Feature Detail

Positional Information

A template with strong edges in both the x and y directions is easier to locate.

a. b.

Figure 5-8. Feature Detail

a. b.

a Good Positional Information in x and y b Insufficient Positional Information in y

Figure 5-9. Positional Information

IMAQ Vision for Visual Basic User Manual 5-14 ni.com

Chapter 5 Performing Machine Vision Tasks

Background Information

Unique background information in a template improves search performance and accuracy.

a. b.

a Pattern with Insufficient Background Information b Pattern with Sufficient Background Information

Figure 5-10. Background Information

Training the Pattern Matching Algorithm

After you create a good template image, the pattern matching algorithm has to learn the important features of the template. Use

CWIMAQVision.LearnPattern2 to learn the template. The learning

process depends on the type of matching that you expect to perform. If you do not expect the instance of the template in the image to rotate or change its size, the pattern matching algorithm has to learn only those features from the template that are necessary for shift-invariant matching. However, if you want to match the template at any orientation, the learning mode must consider the possibility of arbitrary orientations. To specify which type of learning mode to use, pass the learn mode to the

LearnPatternOptions parameter of

CWIMAQVision.LearnPattern2. You also can set the LearnMode

property of a CWIMAQLearnPatternOptions object and pass this object for the

LearnPatternOptions parameter of

CWIMAQVision.LearnPattern2.

The learning process is usually time intensive because the algorithm attempts to find unique features of the template that allow for fast, accurate matching. The learning mode you choose also affects the speed of the learning process. Learning the template for shift-invariant matching is faster than learning for rotation-invariant matching. You also can save time by training the pattern matching algorithm offline, and then saving the template image with

CWIMAQVision.WriteImageAndVisionInfo.

Chapter 5 Performing Machine Vision Tasks

Defining a Search Area

Two equally important factors define the success of a pattern matching algorithm: accuracy and speed. You can define a search area to reduce ambiguity in the search process. For example, if the image has multiple instances of a pattern and only one of them is required for the inspection task, the presence of additional instances of the pattern can produce incorrect results. To avoid this, reduce the search area so that only the appropriate pattern lies within the search area.

In many inspection applications, you have general information about the location of the fiducial. Use this information to define a search area. For example, in a typical component placement application, each printed circuit board (PCB) being tested may not be placed in the same location with the same orientation. The location of the PCB in various images can move and rotate within a known range of values, as illustrated in Figure 5-11. Figure 5-11a shows the template used to locate the PCB in the image. Figure 5-11b shows an image containing a PCB with a fiducial you want to locate. Notice the search area around the fiducial. If you know, before the matching process begins, that the PCB can shift or rotate in the image within a fixed range, as shown in Figure 5-11c and Figure 5-11d, respectively, you can limit the search for the fiducial to a small region of the image.

IMAQ Vision for Visual Basic User Manual 5-16 ni.com

Chapter 5 Performing Machine Vision Tasks

Figure 5-11. Selecting a Search Area for Grayscale Pattern Matching

Setting Matching Parameters and Tolerances

Every pattern matching algorithm makes assumptions about the images and pattern matching parameters used in machine vision applications. These assumptions work for a high percentage of the applications. However, there may be applications in which the assumptions used in the algorithm are not optimal. To efficiently select the best pattern matching parameters for the application, you must have a clear understanding of the application and the images you want to process. The following sections discuss parameters that influence the IMAQ Vision pattern matching algorithm.

Match Mode

You can set the match mode to control how the pattern matching algorithm handles the template at different orientations. If you expect the orientation of valid matches to vary less than ±5° from the template, set

CWIMAQMatchPatternOptions.MatchMode to cwimaqMatchShiftInvariant. Otherwise, set the mode element

cwimaqMatchRotationInvariant.

Note Shift-invariant matching is faster than rotation-invariant matching.

Chapter 5 Performing Machine Vision Tasks

Minimum Contrast

Contrast is the difference between the smallest and largest pixel values in a region. You can set the minimum contrast to potentially increase the speed of the pattern matching algorithm. The pattern matching algorithm ignores all image regions where contrast values fall beneath a set minimum contrast value. If the search image has high contrast but contains some low contrast regions, you can set a high minimum contrast value. Using a high minimum contrast value excludes all areas in the image with low contrast, significantly reducing the region in which the pattern matching algorithm must search. If the search image has low contrast throughout, set a low minimum contrast parameter to ensure that the pattern matching algorithm looks for the template in all regions of the image. Use

CWIMAQMatchPatternOptions.MinimumContrast to set the

minimum contrast.

Rotation Angle Ranges

If you know that the pattern rotation is restricted to a certain range, such as between –15° to 15°, provide this restriction information to the pattern matching algorithm in the

CWIMAQMatchPatternOptions.RotationAngleRanges property.

This information improves your search time because the pattern matching algorithm looks for the pattern at fewer angles. Refer to Chapter 12, Pattern Matching, of the IMAQ Vision Concepts Manual for information about pattern matching.

Testing the Search Algorithm on Test Images

To determine if the selected template or reference pattern is appropriate for the machine vision application, test the template on a few test images. These test images should reflect the images generated by the machine vision application during true operating conditions. If the pattern matching algorithm locates the reference pattern in all cases, you have selected a good template. Otherwise, refine the current template, or select a better template until both training and testing are successful.

IMAQ Vision for Visual Basic User Manual 5-18 ni.com

Using a Ranking Method to Verify Results

The manner in which you interpret the pattern matching results depends on the application. For typical alignment applications, such as finding a fiducial on a wafer, the most important information is the position and bounding rectangle of the best match. Use

CWIMAQPatternMatchReportItem.Position and CWIMAQPatternMatchReportItem.BoundingPoints to get

the position and location of a match.

In inspection applications, such as optical character verification (OCV), the score of the best match is more useful. The score of a match returned by the pattern matching method is an indicator of the closeness between the original pattern and the match found in the image. A high score indicates a very close match, while a low score indicates a poor match. The score can be used as a gauge to determine if a printed character is acceptable. Use

CWIMAQPatternMatchReportItem.Score to get a match score.

Finding Points Using Color Pattern Matching

Color pattern matching algorithms provide a quick way to locate objects when color is present. Use color pattern matching under the following circumstances:

• The object you want to locate has color information that is very different from the background, and you want to find a very precise location of the object in the image.

• The object to locate has grayscale properties that are very difficult to characterize or that are very similar to other objects in the search image. In such cases, grayscale pattern matching can give inaccurate results. If the object has color information that differentiates it from the other objects in the scene, color provides the machine vision software with the additional information to locate the object.

Chapter 5 Performing Machine Vision Tasks

Color pattern matching returns the location of the center of the template and the template orientation. Follow these general steps to find features in an image using color pattern matching:

1. Define a reference or fiducial pattern in the form of a template image.

2. Use the reference pattern to train the color pattern matching algorithm with

CWIMAQVision.LearnColorPattern.

3. Define an image or an area of an image as the search area. A small search area reduces the time to find the features.

4. Set

CWIMAQMatchColorPatternOptions.FeatureMode to

cwimaqFeatureAll.

Chapter 5 Performing Machine Vision Tasks

5. Set the tolerances and parameters to specify how the algorithm operates at run time using

6. Test the search algorithm on test images using

CWIMAQVision.MatchColorPattern.

7. Verify the results using a ranking method.

Defining and Creating Effective Color Template Images

The selection of a effective template image plays a critical part in obtaining accurate results with the color pattern matching algorithm. Because the template image represents the color and the pattern that you want to find, make sure that all the important and unique characteristics of the pattern are well defined in the image.

Several factors are critical in creating a template image. These critical factors include color information, symmetry, feature detail, positional information, and background information.

Color Information

A template with colors that are unique to the pattern provides better results than a template that contains many colors, especially colors found in the background or other objects in the image.

CWIMAQMatchColorPatternOptions.

Symmetry

A rotationally symmetric template in the luminance plane is less sensitive to changes in rotation than one that is rotationally asymmetric.

Feature Detail

A template with relatively coarse features is less sensitive to variations in size and rotation than a model with fine features. However, the model must contain enough detail to identify it.

Positional Information

A template with strong edges in both the x and y directions is easier to locate.

IMAQ Vision for Visual Basic User Manual 5-20 ni.com

Chapter 5 Performing Machine Vision Tasks

Background Information

Unique background information in a template improves search performance and accuracy during the grayscale pattern matching phase. This requirement could conflict with the “color information” requirement because background colors may not be appropriate during the color location phase. Avoid this problem by choosing a template with sufficient background information for grayscale pattern matching while specifying the exclusion of the background color during the color location phase. Refer to the Training the Pattern Matching Algorithm section of this chapter for more information about how to ignore colors.

Training the Color Pattern Matching Algorithm

After you have created a good template image, the color pattern matching algorithm learns the important features of the template. Use

CWIMAQVision.LearnColorPattern to learn the template. The

learning process depends on the type of matching that you expect to perform. By default, the color pattern matching algorithm learns only those features from the template that are necessary for shift-invariant matching. However, if you want to match the template at any orientation, the learning process must consider the possibility of arbitrary orientations. Use the

CWIMAQLearnColorPatternOptions.LearnMode property to specify

which type of learning mode to use.

Exclude colors in the template that you are not interested in using during the search phase. Typically, you should ignore colors that either belong to the background of the object or are not unique to the template, reducing the potential for incorrect matches during the color location phase. You can learn the colors to ignore using

CWIMAQLearnColorPatternOptions.IgnoreBlackAndWhite or CWIMAQLearnColorPatternOptions.IgnoreColorSpectra

CWIMAQVision.LearnColor. Use the

properties to ignore background colors.

The training or learning process is time-intensive because the algorithm attempts to find optimal features of the template for the particular matching process. However, you can train the pattern matching algorithm offline, and save the template image using

CWIMAQVision.WriteImageAndVisionInfo.

Chapter 5 Performing Machine Vision Tasks

Defining a Search Area

Two equally important factors define the success of a color pattern matching algorithm—accuracy and speed. You can define a search area to reduce ambiguity in the search process. For example, if the image has multiple instances of a pattern and only one instance is required for the inspection task, the presence of additional instances of the pattern can produce incorrect results. To avoid this, reduce the search area so that only the appropriate pattern lies within the search area. For example, in the fuse box inspection example use the location of the fuses to be inspected to define the search area. Because the inspected fuse box may not be in the exact location or have the same orientation in the image as the previous one, the search area you define should be large enough to accommodate these variations in the position of the box. Figure 5-12 shows how search areas can be selected for different objects.

1 2

1 Search Area for 20 Amp Fuses 2 Search Area for 25 Amp Fuses

Figure 5-12. Selecting a Search Area for Color Pattern Matching

IMAQ Vision for Visual Basic User Manual 5-22 ni.com

Chapter 5 Performing Machine Vision Tasks

The time required to locate a pattern in an image depends on both the template size and the search area. By reducing the search area or increasing the template size, you can reduce the required search time. Increasing the size of the template can improve search time, but doing so reduces match accuracy if the larger template includes an excess of background information.

Setting Matching Parameters and Tolerances

Every color pattern matching algorithm makes assumptions about the images and color pattern matching parameters used in machine vision applications. These assumptions work for a high percentage of the applications.

In some applications, the assumptions used in the algorithm are not optimal. In such cases, you must modify the color pattern matching parameters. To efficiently select the best pattern matching parameters for the application, you must have a clear understanding of the application and the images you want to process.

The following sections discuss parameters of the IMAQ Vision color pattern matching algorithm, and how they influence the algorithm.

Color Sensitivity

Use the color sensitivity to control the granularity of the color information in the template image. If the background and objects in the image contain colors that are very close to colors in the template image, use a higher color sensitivity setting. A higher sensitivity setting distinguishes colors with very close hue values. Three color sensitivity settings are available in IMAQ Vision: low, medium, and high. Use the low setting, which is the default, if the colors in the template are very different from the colors in the background or other objects that you are not interested in. Increase the color sensitivity settings as the color differences decrease. Use

CWIMAQMatchColorPatternOptions.ColorSensitivity to set the

color sensitivity. For information about color sensitivity, refer to Chapter 14, Color Inspection, of the IMAQ Vision Concepts Manual.

Search Strategy

Use the search strategy to optimize the speed of the color pattern matching algorithm. The search strategy controls the step size, sub-sampling factor, and the percentage of color information used from the template.

Chapter 5 Performing Machine Vision Tasks

Use one of the following four search strategies:

• Very aggressive—Uses the largest step size, the most sub-sampling and only the dominant color from the template to search for the template. Use this strategy when the color in the template is almost uniform, the template is well contrasted from the background and there is a good amount of separation between different occurrences of the template in the image. This strategy is the fastest way to find templates in an image.

• Aggressive—Uses a large step size, a large amount of subsampling, and all the color spectrum information from the template.

• Balanced—Uses values in between the aggressive and conservative strategies.

• Conservative—Uses a very small step size, the least amount of subsampling, and all the color information present in the template. The conservative strategy is the most reliable method to look for a template in any image at potentially reduced speed.

Note Use the conservative strategy if you have multiple targets located very close to each

other in the image.

Decide on the best strategy by experimenting with the different options. Use

CWIMAQMatchColorPatternOptions.SearchStrategy to select

a search strategy.

Color Score Weight

When you search for a template using both color and shape information, the color and shape scores generated during the match process are combined to generate the final color pattern matching score. The color score weight determines the contribution of the color score to the final color pattern matching score. If the template color information is superior to its shape information, set the weight higher. For example, if you use a weight of 1000, the algorithm finds each match by using both color and shape information, and then ranks the matches based entirely on their color scores. If the weight is 0, the matches are ranked based entirely on only their shape scores. Use the

CWIMAQMatchColorPatternOptions.ColorScoreWeight

property to set the color score weight.

IMAQ Vision for Visual Basic User Manual 5-24 ni.com

Chapter 5 Performing Machine Vision Tasks

Minimum Contrast

Use the minimum contrast to increase the speed of the color pattern matching algorithm. The color pattern matching algorithm ignores all image regions where grayscale contrast values fall beneath a set minimum contrast value. Use

CWIMAQMatchColorPatternMatchingOptions.MinimumContrast

to set the minimum contrast. Refer to the Setting Matching Parameters and

Tolerances section of this chapter for more information about minimum

contrast.

Rotation Angle Ranges

If you know that the pattern rotation is restricted to a certain range, provide this restriction information to the pattern matching algorithm by using the

CWIMAQMatchPatternOptions.RotationAngleRanges property.

This information improves the search time because the color pattern matching algorithm looks for the pattern at fewer angles. Refer to Chapter 12, Pattern Matching, in the IMAQ Vision Concepts Manual for more information about pattern matching.

Testing the Search Algorithm on Test Images

To determine if the selected template or reference pattern is appropriate for the machine vision application, test the template on a few test images by using the images should reflect the images generated by the machine vision application during true operating conditions. If the color pattern matching algorithm locates the reference pattern in all cases, you have selected a good template. Otherwise, refine the current template, or select a better template until both training and testing are successful.

CWIMAQVision.MatchColorPattern method. These test

Finding Points Using Color Location

Color location algorithms provide a quick way to locate regions in an image with specific colors.

Use color location under the following circumstances:

• Requires the location and the number of regions in an image with their specific color information

• Relies on the cumulative color information in the region, instead of the color arrangement in the region

• Does not require the orientation of the region

Chapter 5 Performing Machine Vision Tasks

• Does not always require the location with sub-pixel accuracy

• Does not require shape information for the region

Complete the following steps to find features in an image using color location:

1. Define a reference pattern in the form of a template image.

2. Use the reference pattern to train the color location algorithm with

CWIMAQVision.LearnColorPattern.

3. Define an image or an area of an image as the search area. A small search area reduces the time to find the features.

4. Set

5. Set the tolerances and parameters to specify how the method operates

6. Use

7. Verify the results using a ranking method.

Use image.

CWIMAQMatchColorPatternOptions.FeatureMode to

cwimaqFeatureColorInformation.

at run time using

CWIMAQVision.MatchColorPattern to test the color location

algorithm on test images.

CWIMAQVision.WriteImageAndVisionInfo to save the template

CWIMAQMatchColorPatternOptions.

Convert Pixel Coordinates to Real-World Coordinates

The measurement points you located with edge detection and pattern matching are in pixel coordinates. If you need to make measurements using real-world units, use

CWIMAQVision.ConvertPixelToRealWorldCoordinates to convert

the pixel coordinates into real-world units.

Make Measurements

You can make different types of measurements either directly from the image or from points that you detect in the image.

Distance Measurements

Use the following methods to make distance measurements for the inspection application.

Clamp methods measure the separation between two edges in a rectangular search region. First, clamp methods detect points along the two edges using

IMAQ Vision for Visual Basic User Manual 5-26 ni.com

the rake method, and then they compute the distance between the points detected on the edges along each search line of the rake and return the largest or smallest distance in either the horizontal or vertical direction. The MeasurementAxis parameter specifies the axis along which to measure. You also need to specify the parameters for edge detection and the separation between the search lines that you want to use within the search region to find the edges. These methods work directly on the image under inspection, and they output the coordinates of all the edge points that they find. The following list describes the available clamp methods:

•

CWMachineVision.MeasureMaximumDistance—Measures the

largest separation between two edges in a rectangular search region.

•

CWMachineVision.MeasureMinimumDistance—Measures the

smallest separation between two edges in a rectangular search region.

Use

CWIMAQVision.FindPointDistances to compute the distances

between consecutive pairs of points in an array of points. You can obtain these points from the image using any one of the feature detection methods described in the Find Measurement Points section of this chapter.

Analytic Geometry Measurements

Use the following CWIMAQVision methods to make geometrical measurements from the points you detect in the image:

•

FitLine—Fits a line to a set of points and computes the equation of

the line.

•

FitCircle2—Fits a circle to a set of at least three points and

computes its area, perimeter and radius.

•

FitEllipse2—Fits an ellipse to a set of at least six points and

computes its area, perimeter, and the lengths of its major and minor axis.

•

FindIntersectionPoint—Finds the intersection point of two lines

specified by their start and end points.

•

FindAngleBetweenLines—Finds the smaller angle between two

lines.

•

FindPerpendicularLine—Finds the perpendicular line from a

point to a line.

•

FindDistanceFromPointToLine—Computes the perpendicular

distance between the point and the line.

•

FindBisectingLine—Finds the line that bisects the angle formed

by two lines.

Chapter 5 Performing Machine Vision Tasks

• FindMidLine—Finds the line that is midway between a point and a line and is parallel to the line.

•

FindPolygonArea—Calculates the area of a polygon specified by its

vertex points.

Instrument Reader Measurements

You can make measurements based on the values obtained by meter, LCD, and barcode readers.

Use

CWIMAQMeterArc.CreateFromPoints or

CWIMAQMeterArc.CreateFromLines to calibrate a meter or

gauge that you want to read. calibrates the meter using the initial position and the full-scale position of the needle. meter using three points on the meter: the base of the needle, the tip of the needle at its initial position, and the tip of the needle at its full-scale position. Use needle using the CWIMAQMeterArc object.

Use

CWIMAQVision.FindLCDSegments to calculate the regions of

interest around each digit in an LCD or LED. To find the area of each digit, all the segments of the indicator must be activated. Use

CWIMAQVision.ReadLCD to read the digits of an LCD or LED.

CWIMAQMeterArc.CreateFromPoints calibrates the

CWIMAQVision.ReadMeter to read the position of the

CWIMAQMeterArc.CreateFromLines

Identify Parts Under Inspection

In addition to making measurements after you set regions of inspection, you also can identify parts using classification, OCR, and barcode reading.

Classifying Samples

Use classification to identify an unknown object by comparing a set of its significant features to a set of features that conceptually represent classes of known objects. Typical applications involving classification include the following:

• Sorting—Sorts objects of varied shapes. For example, sorting different mechanical parts on a conveyor belt into different bins.

• Inspection—Inspects objects by assigning each object an identification score and then rejecting objects that do not closely match members of the training set.

IMAQ Vision for Visual Basic User Manual 5-28 ni.com

Reading Characters

Chapter 5 Performing Machine Vision Tasks

Before you classify objects, you must create a classifier file with samples of the objects using the NI Classification Training Interface. Go to Start» Programs»National Instruments»Classification Training to launch the NI Classification Training Interface.

After you have trained samples of the objects you want to classify, use the following methods to classify the image under inspection:

•Use

Use OCR to read text and/or characters in an image. Typical uses for OCR in an inspection application include identifying or classifying components.

Before you read text and/or characters in an image, you must create a character set file with samples of the characters using the OCR Training Interface. Go to Start»Programs»National Instruments»Vision»OCR Training to launch the OCR Training Interface.

CWIMAQVision.ReadClassifierFile to read in the classifier

file you created with the NI Classification Training Interface.

CWIMAQClassifier.Classify to classify the image under

inspection.

After you have trained samples of the characters you want to read, use the following methods to read the characters:

•Use

NIOCR.ReadOCRFile to read in a character set file that you

created using the OCR Training Interface.

NIOCR.ReadText to read the characters inside the ROI of the

image under inspection.

Reading Barcodes

Use barcode reading objects to read values encoded into 1D barcodes, Data Matrix symbols, and PDF417 symbols.

Read 1D Barcodes

To read a 1D barcode, locate the barcode in the image using one of the techniques described in the Instrument Reader Measurements section, and then pass the Regions parameter into

Use

CWIMAQVision.ReadBarcode to read values encoded in the 1D

barcode. Specify the type of 1D barcode in the application using the BarcodeType parameter. IMAQ Vision supports the following 1D barcode

CWIMAQVision.ReadBarcode.

Chapter 5 Performing Machine Vision Tasks

types: Codabar, Code 39, Code 93, Code 128, EAN 8, EAN 13, Interleaved 2 of 5, MSI, and UPCA.

Read Data Matrix Barcode

Use CWIMAQVision.ReadDataMatrixBarcode to read values encoded in a Data Matrix barcode. This method can automatically determine the location of the barcode and appropriate search options for the application. However, you can improve the performance of the application by specifying control values specific to the application.

CWIMAQVision.ReadDataMatrixBarcode can automatically locate

one or multiple Data Matrix barcodes in an image. However, you can improve the inspection performance by locating the barcodes using one of the techniques described in the Instrument Reader Measurements section, and then passing the Regions parameter into

CWIMAQVision.ReadDataMatrixBarcode.

Tip If you need to read only one barcode per image, set

CWIMAQDataMatrixOptions.SearchMode to cwimaqBarcode2DSearchSingleConservative to increase the speed of the method.

By default,

CWIMAQVision.ReadDataMatrixBarcode determines if the

barcode has black cells on a white background or white cells on a black background.

Note Specify round cells only if the Data Matrix cells are round and have clearly defined

edges. If the cells in the matrix touch one another, you must set

cwimaqBarcode2DCellShapeSquare.

By default,

CWIMAQVision.ReadDataMatrixBarcode assumes the

CellShape to

barcode cells are square. If the barcodes you need to read have round cells, set the

CellShape member of the CWIMAQDataMatrixOptions object to

cwimaqBarcode2DCellShapeRound.

Set the object to

cwimaqBarcode2DShapeSquare depending on the shape of the

BarcodeShape member of the CWIMAQDataMatrixOptions

cwimaqBarcode2DShapeRectangular or

barcode you need to read.

Note Setting the BarcodeShape member of the CWIMAQDataMatrixOptions object to cwimaqBarcode2DShapeRectangular when the barcode you need to read is square

reduces the reliability of the application.

IMAQ Vision for Visual Basic User Manual 5-30 ni.com

Chapter 5 Performing Machine Vision Tasks

By default, CWIMAQVision.ReadDataMatrixBarcode automatically detects the type of barcode to read. You can improve the performance of the function by specifying the type of barcode in the application. IMAQ Vision supports Data Matrix types ECC 000 to ECC 140, and ECC 200.

Read PDF417 Barcode

Use CWIMAQVision.ReadPDF417Barcode to read values encoded in a PDF417 barcode.

Tip If you need to read only one barcode per image, set the SearchMode parameter to

cwimaqBarcode2DSearchSingleConservative to increase the speed of the method.

Display Results

Note The CWIMAQImage.Overlays collection does not support usual collection

methods—such as future use.

By default,

CWIMAQVision.ReadPDF417Barcode automatically locates

one or multiple PDF417 barcodes in an image. However, you can improve the inspection performance by locating the barcodes using one of the techniques described in the Instrument Reader Measurements section, and then passing in Regions of the locations into

CWIMAQVision.ReadPDF417Barcode.

You can display the results obtained at various stages of the inspection process on the window that displays the inspection image by overlaying information about an image. The software attaches the information that you want to overlay to the image, but it does not modify the image. Access overlays using the

CWIMAQImage.Overlays property. The

CWIMAQOverlays collection contains a single CWIMAQOverlay object that you can access using

Add, Remove, and RemoveAll—because they are reserved for

CWIMAQImage.Overlay(1).

Use the following methods on the CWIMAQOverlay object to overlay search regions, inspection results, and other information, such as text and pictures. Overlays on a viewer image are automatically updated when you call one of these methods.

•

DrawLine—Overlays a CWIMAQLine object on an image.

•

DrawConnectedPoints—Overlays a CWIMAQPoints collection

and draws a line between sequential points.

Chapter 5 Performing Machine Vision Tasks

• DrawRectangle—Overlays a CWIMAQRectangle object on an image.

•

DrawOval—Overlays a CWIMAQOval object on an image.

•

DrawArc—Overlays a CWIMAQArc object on an image.

•

DrawPicture—Overlays a picture object onto the image.

•

DrawText—Overlays text on an image.

•

DrawRegions—Overlays an ROI described by the CWIMAQRegions

object on an image.

Tip You can select the color of overlays by using one of these methods. If you do not

supply a color to an overlay method, the is used.

You can configure the following CWMachineVision methods to overlay different types of information about the inspection image:

•

FindStraightEdge

• FindCircularEdge

• FindConcentricEdge

• MeasureMaximumDistance

• MeasureMinimumDistance

• FindPattern

• CountAndMeasureObjects

• FindCoordTransformUsingRect

• FindCoordTransformUsingTwoRects

• FindCoordTransformUsingPattern

CWIMAQOverlay.DefaultColor property

You can overlay the following information with all the above methods except

CWMachineVision.FindPattern:

• The search area input into the method

• The search lines used for edge detection

• The edges detected along the search lines

• The result of the method

Each of the above CWMachineVision methods has a settings object input that allows you to select the information you want to overlay. Set the boolean property that corresponds to the information you want to overlay

IMAQ Vision for Visual Basic User Manual 5-32 ni.com

Chapter 5 Performing Machine Vision Tasks

to True. With CWMachineVision.FindPattern, you can overlay the search area and the result.

Use

CWIMAQOverlay.Clear to clear any previous overlay information

from the image. Use

CWIMAQVision.WriteImageAndVisionInfo

to save an image with its overlay information to a file. You can read the information from the file into an image using the

CWIMAQVision.ReadImageAndVisionInfo.

Note As with calibration information, overlay information is removed from an image

when the image size or orientation changes.

Calibrating Images

This chapter describes how to calibrate the imaging system, save calibration information, and attach calibration information to an image.

After you set up the imaging system, you may want to calibrate the system. If the imaging setup is such that the camera axis is perpendicular or nearly perpendicular to the object under inspection and the lens has no distortion, use simple calibration. With simple calibration, you do not need to learn a template. Instead, you define the distance between pixels in the horizontal and vertical directions using real-world units.

If the camera axis is not perpendicular to the object under inspection or the lens is distorted, use perspective and nonlinear distortion calibration to calibrate the system.

Perspective and Nonlinear Distortion Calibration

Perspective errors and lens aberrations cause images to appear distorted. This distortion misplaces information in an image, but it does not necessarily destroy the information in the image. Calibrate the imaging system if you need to compensate for perspective errors or nonlinear lens distortion.

Follow these general steps to calibrate the imaging system:

1. Define a calibration template.

2. Define a reference coordinate system.

3. Learn the calibration information.

After you calibrate the imaging setup, you can attach the calibration information to an image. Refer to the Attach Calibration Information section of this chapter for more information. Depending on your needs, you can either apply calibration information in one of the following ways:

• Convert pixel coordinates to real-world coordinates without correcting the image

• Create a distortion-free image by correcting the image for perspective errors and lens aberrations

Chapter 6 Calibrating Images

Refer to Chapter 5, Performing Machine Vision Tasks, for more information about applying calibration information before making measurements.

Defining a Calibration Template

You can define a calibration template by supplying an image of a grid or providing a list of pixel coordinates and their corresponding real-world coordinates. This section discusses the grid method in detail.

A calibration template is a user-defined grid of circular dots. As shown in Figure 6-1, the grid has constant spacings in the x and y directions. You can use any grid, but follow these guidelines for the best results:

• The displacement in the x and y directions must equal (dx = dy).

• The dots must cover the appropriate portion of the working area.

• The radius of the dots must be 6–10 pixels.

• The center-to-center distance between dots must range from 18 to 32 pixels, as shown in Figure 6-1.

• The minimum distance between the edges of the dots must be 6 pixels, as shown in Figure 6-1.

1 Center-to-Center Distance 2 Center of Grid Dots 3 Distance Between Dot Edges

Figure 6-1. Defining a Calibration Grid

Note

You can use the calibration grid installed with IMAQ Vision at Start»Programs»

National Instruments»Vision»Documentation»Calibration Grid. The dots have radii of 2 mm and center-to-center distances of 1 cm. Depending on the printer, these measurements may change by a fraction of a millimeter. You can purchase highly accurate calibration grids from optics suppliers, such as Edmund Industrial Optics.

IMAQ Vision for Visual Basic User Manual 6-2 ni.com

Defining a Reference Coordinate System

To express measurements in real-world units, you must define a coordinate system in the image of the grid. Use

CWIMAQLearnCalibrationOptions.CalibrationAxisInfo

to define a coordinate system by its origin, angle, and axis direction.

The origin, expressed in pixels, defines the center of the coordinate system. The angle specifies the orientation of the coordinate system with respect to the angle of the topmost row of dots in the grid image. The calibration procedure automatically determines the direction of the horizontal axis in the real world. The vertical axis direction can either be indirect, as shown in Figure 6-2a, or direct, as shown in Figure 6-2b.

Chapter 6 Calibrating Images

a. b.

Figure 6-2. Axis Direction in the Image Plane

If you do not specify a coordinate system, the calibration process defines a default coordinate system. If you specify a grid for the calibration process, the software defines the following default coordinate system, as shown in Figure 6-3:

1. The origin is placed at the center of the left, topmost dot in the calibration grid.

2. The angle is set to 0°. This aligns the x-axis with the first row of dots in the grid, as shown in Figure 6-3b.

3. The axis direction is set to indirect using

CWIMAQCoordinateSystem.AxisOrientation=

cwimaqAxisOrientationIndirect

. This aligns the y-axis to the

first column of the dots in the grid, as shown in Figure 6-3b.

Chapter 6 Calibrating Images

1 Origin of a Calibration Grid in the Real World 2 Origin of the Same Calibration Grid in an Image

Figure 6-3. A Calibration Grid and an Image of the Grid

If you specify a list of points instead of a grid for the calibration process,

Note

the software defines a default coordinate system, as follows:

1. The origin is placed at the point in the list with the lowest x-coordinate value and then the lowest y-coordinate value.

2. The angle is set to 0°.

3. The axis direction is set to indirect using

CWIMAQCoordinateSystem.AxisOrientation=

cwimaqAxisOrientationIndirect

If you define a coordinate system yourself, carefully consider the requirements of the application:

• Express the origin in pixels. Always choose an origin location that lies within the calibration grid so that you can convert the location to real-world units.

• Specify the angle as the angle between the x-axis of the new coordinate system (x') and the top row of dots (x), as shown in Figure 6-4. If the imaging system exhibits nonlinear distortion, you cannot visualize the angle as you can in Figure 6-4 because the dots do not appear in straight lines.

IMAQ Vision for Visual Basic User Manual 6-4 ni.com

Chapter 6 Calibrating Images

1 Default Origin in a Calibration Grid Image 2 User-Defined Origin

Learning Calibration Information

After you define a calibration grid and reference axis, acquire an image of the grid using the current imaging setup. For information about acquiring images, refer to the Acquire or Read an Image section of Chapter 2, Getting

Measurement-Ready Images. The grid does not need to occupy the entire

image. You can choose a region within the image that contains the grid. After you acquire an image of the grid, learn the calibration information by inputting the image of the grid into

CWIMAQVision.LearnCalibrationGrid.

Note If you want to specify a list of points instead of a grid, use

CWIMAQVision.LearnCalibrationPoints to learn the calibration information.

Use the CWIMAQCalibrationPoints object to specify the pixel to real-world mapping.

Figure 6-4. Defining a Coordinate System

Chapter 6 Calibrating Images

Note The user-defined ROI represents the area in which you are interested. The learning

ROI is separate from the calibration ROI that is generated by the calibration algorithm. Refer to Figure 6-6 for an illustration of calibration ROIs.

Specifying Scaling Factors

Scaling factors are the real-world distances between the dots in the calibration grid in the x and y directions and the units in which the distances are measured. Use

CWIMAQCalibrationGridOptions.GridDescriptor to

specify the scaling factors.

Choosing a Region of Interest

Define a learning ROI during the learning process to define a region of the calibration grid you want to learn. The software ignores dot centers outside this region when it estimates the transformation. Creating a user-defined ROI is an effective way to increase correction speeds depending on the other calibration options selected. Pass a CWIMAQRegions collection representing the region you want to learn to the

CWIMAQVision.LearnCalibrationGrid or CWIMAQVision.LearnCalibrationPoints.

Regions parameter of

Choosing a Learning Algorithm

Select a method in which to learn the calibration information: perspective projection or nonlinear. Figure 6-5 illustrates the types of errors the image can exhibit. Figure 6-5a shows an image of a calibration grid with no errors. Figure 6-5b shows an image of a calibration grid with perspective projection. Figure 6-5c shows an image of a calibration grid with nonlinear distortion.

a. c.b.

Figure 6-5. Types of Image Distortion

IMAQ Vision for Visual Basic User Manual 6-6 ni.com

Chapter 6 Calibrating Images

Choose the perspective projection algorithm when the system exhibits perspective errors only. A perspective projection calibration has an accurate transformation even in areas not covered by the calibration grid, as shown in Figure 6-6. Set

CWIMAQLearnCalibrationOptions.CalibrationMethod to cwimaqPerspectiveCalibration to choose the perspective calibration

algorithm. Learning and applying perspective projection is less computationally intensive than the nonlinear method. However, perspective projection cannot handle nonlinear distortions.

If the imaging setup exhibits nonlinear distortion, use the nonlinear method. The nonlinear method guarantees accurate results only in the area that the calibration grid covers, as shown in Figure 6-6. If the system exhibits both perspective and nonlinear distortion, use the nonlinear method to correct for both. Set

CWIMAQLearnCalibrationOptions.CalibrationMethod to cwimaqNonLinearCalibration to chose the nonlinear calibration

algorithm.

1 Calibration ROI Using the

Perspective Algorithm

Figure 6-6. Calibration ROIs

2 Calibration ROI Using the

Nonlinear Algorithm

Using the Learning Score

The learning process returns a score that reflects how well the software learned the input image. A high learning score indicates that you chose the the appropriate learning algorithm, that the grid image complies with the guideline, and that the vision system setup is adequate.

Chapter 6 Calibrating Images

Note A high score does not reflect the accuracy of the system.

If the learning process returns a learning score below 600, try the following:

1. Make sure the grid complies with the guidelines listed in the

Defining a Calibration Template section.

2. Check the lighting conditions. If you have too much or too little lighting, the software may estimate the center of the dots incorrectly. Also, adjust the threshold range to distinguish the dots from the background.

3. Select another learning algorithm. When nonlinear lens distortion is present, using perspective projection sometimes results in a low learning score.

Learning the Error Map

An error map helps you gauge the quality of the complete system. The error map returns an estimated error range to expect when a pixel coordinate is transformed into a real-world coordinate. The transformation accuracy may be higher than the value the error range indicates. Set

CWIMAQLearnCalibrationOptions.LearnErrorMap to True to learn

the error map.

Learning the Correction Table

If the speed of image correction is a critical factor for the application, use a correction table. The correction table is a lookup table that contains the real-world location information of all the pixels in the image. The correction table is stored in memory. The extra memory requirements for this option are based on the size of the image. Use this option when you want to simultaneously correct multiple images in the vision application. Set

CWIMAQLearnCalibrationOptions.LearnCorrectionTable to

True to learn the correction table.

Setting the Scaling Mode

Use the scaling mode option to choose the appearance of the corrected image. Set

CWIMAQLearnCalibrationOptions.CorrectionScalingMode to cwimaqScaleToFit or cwimaqScaleToPreserveArea. For more

information about the scaling mode, refer to Chapter 3, System Setup and Calibration, in the IMAQ Vision Concepts Manual.

IMAQ Vision for Visual Basic User Manual 6-8 ni.com

Calibration Invalidation

Any image processing operation that changes the image size or orientation voids the calibration information in a calibrated image. Examples of methods that void calibration information include

CWIMAQVision.Resample2, CWIMAQVision.Extract2, CWIMAQVision.Unwrap, and CWIMAQImage.ArrayToImage.

Simple Calibration

When the axis of the camera is perpendicular to the image plane and lens distortion is negligible, use simple calibration. In simple calibration, a pixel coordinate is transformed into a real-world coordinate through scaling in the horizontal and vertical directions.

Use simple calibration to map pixel coordinates to real-world coordinates directly without a calibration grid. The software rotates and scales a pixel coordinate according to predefined coordinate reference and scaling factors. You can assign the calibration to an arbitrary image using

CWIMAQVision.SetSimpleCalibration.

To perform a simple calibration, set a coordinate system (angle, center, and axis direction) and scaling factors on the defined axis, as shown in Figure 6-7. Express the angle between the x-axis and the horizontal axis of the image in degrees. Express the center as the position, in pixels, where you want the coordinate system origin. Set the axis direction to direct or indirect. Simple calibration also offers a correction table option and a scaling mode option.

Chapter 6 Calibrating Images

Use

CWIMAQSimpleCalibrationOptions.CalibrationAxisInfo

to define the coordinate reference. Use

CWIMAQSimpleCalibrationOptions.GridDescriptor

to specify the scaling factors. Use

CWIMAQSimpleCalibrationOptions.CorrectionScalingMode

to set the scaling mode. Set

CWIMAQSimpleCalibrationOptions.LearnCorrectionTable

True to learn the correction table.

Chapter 6 Calibrating Images

1Origin

Figure 6-7. Defining a Simple Calibration

Save Calibration Information

After you learn the calibration information, you can save it so that you do not have to relearn the information for subsequent processing. Use

CWIMAQVision.WriteImageAndVisionInfo to save the image of

the grid and its associated calibration information to a file. To read the file containing the calibration information use

CWIMAQVision.ReadImageAndVisionInfo. For more information

about attaching the calibration information you read from another image, refer to the Attach Calibration Information section.

Attach Calibration Information

When you finish calibrating the setup, you can apply the calibration settings to images that you acquire. Use

CWIMAQVision.SetCalibrationInformation to attach the

calibration information of the current setup to each image you acquire. This method takes in a source image containing the calibration information and a destination image that you want to calibrate. The output image is the inspection image with the calibration information attached to it.

Using the calibration information attached to the image, you can accurately convert pixel coordinates to real-world coordinates to make any of the analytic geometry measurements with

IMAQ Vision for Visual Basic User Manual 6-10 ni.com

Chapter 6 Calibrating Images

CWIMAQVision.ConvertPixelToRealWorldCoordinates. If

the application requires shape measurements, correct the image by removing distortion with

Note Correcting images is a time-intensive operation.

CWIMAQVision.CorrectCalibratedImage.

A calibrated image is different from a corrected image.

Note Because calibration information is part of the image, it is propagated throughout

the processing and analysis of the image. Methods that modify the image size, such as an image rotation method, void the calibration information. Use

CWIMAQVision.WriteImageAndVisionInfo to save the image and all of the attached

calibration information to a file. If you modify the image after using

CWIMAQVision.WriteImageAndVisionInfo, you must relearn the calibration

information and use

CWIMAQVision.WriteImageAndVisionInfo again.

Technical Support and Professional Services

Visit the following sections of the National Instruments Web site at

ni.com for technical support and professional services:

• Support—Online technical support resources at

include the following:

– Self-Help Resources—For immediate answers and solutions,

visit the award-winning National Instruments Web site for software drivers and updates, a searchable KnowledgeBase, product manuals, step-by-step troubleshooting wizards, thousands of example programs, tutorials, application notes, instrument drivers, and so on.

– Free Technical Support—All registered users receive free Basic

Service, which includes access to hundreds of Application Engineers worldwide in the NI Developer Exchange at

ni.com/exchange. National Instruments Application Engineers

make sure every question receives an answer.

• Training and Certification—Visit

self-paced training, eLearning virtual classrooms, interactive CDs, and Certification program information. You also can register for instructor-led, hands-on courses at locations around the world.

• System Integration—If you have time constraints, limited in-house

technical resources, or other project challenges, National Instruments Alliance Partner members can help. To learn more, call your local NI office or visit

ni.com/alliance.

ni.com/support

ni.com/training for

If you searched your local office or NI corporate headquarters. Phone numbers for our worldwide offices are listed at the front of this manual. You also can visit the Worldwide Offices section of office Web sites, which provide up-to-date contact information, support phone numbers, email addresses, and current events.

ni.com and could not find the answers you need, contact

ni.com/niglobal to access the branch

Glossary

Numbers

1D One-dimensional.

2D Two-dimensional.

3D Three-dimensional.

AIPD The National Instruments internal image file format used for saving

complex images and calibration information associated with an image (extension APD).

alignment The process by which a machine vision application determines the location,

orientation, and scale of a part being inspected.

alpha channel The channel used to code extra information, such as gamma correction,

about a color image. The alpha channel is stored as the first byte in the four-byte representation of an RGB pixel.

area (1) A rectangular portion of an acquisition window or frame that is

controlled and defined by software.

(2) The size of an object in pixels or user-defined units.

arithmetic operators The image operations multiply, divide, add, subtract, and modulo.

array An ordered, indexed set of data elements of the same type.

auto-median function A function that uses dual combinations of opening and closing operations

to smooth the boundaries of objects.

b Bit. One binary digit, either 0 or 1.

B Byte. Eight related bits of data, an eight-bit binary number. Also denotes

the amount of memory required to store one byte of data.

Glossary

barycenter The grayscale value representing the centroid of the range of an image’s

grayscale values in the image histogram.

binary image An image in which the objects usually have a pixel intensity of 1 (or 255)

and the background has a pixel intensity of 0.

binary morphology Functions that perform morphological operations on a binary image.

binary threshold The separation of an image into objects of interest (assigned a pixel value

of 1) and background (assigned pixel values of 0) based on the intensities of the image pixels.

bit depth The number of bits (n) used to encode the value of a pixel. For a given n,

a pixel can take 2

different values. For example, if n equals 8, a pixel can take 256 different values ranging from 0 to 255. If n equals 16, a pixel can take 65,536 different values ranging from 0 to 65,535 or –32,768 to 32,767.

blurring Reduces the amount of detail in an image. Blurring commonly occurs

because the camera is out of focus. You can blur an image intentionally by applying a lowpass frequency filter.

BMP Bitmap. An image file format commonly used for 8-bit and color images.

BMP images have the file extension BMP.

border function Removes objects (or particles) in a binary image that touch the image

border.

brightness (1) A constant added to the red, green, and blue components of a color pixel

during the color decoding process.

(2) The perception by which white objects are distinguished from gray and light objects from dark objects.

buffer Temporary storage for acquired data.

IMAQ Vision for Visual Basic User Manual G-2 ni.com

Glossary

caliper (1) A function in the NI Vision Assistant and in NI Vision Builder for

Automated Inspection that calculates distances, angles, circular fits, and the center of mass based on positions given by edge detection, particle analysis, centroid, and search functions.

(2) A measurement function that finds edge pairs along a specified path in the image. This function performs an edge extraction and then finds edge pairs based on specified criteria such as the distance between the leading and trailing edges, edge contrasts, and so forth.

center of mass The point on an object where all the mass of the object could be

concentrated without changing the first moment of the object about any axis.

chroma The color information in a video signal.

chromaticity The combination of hue and saturation. The relationship between

chromaticity and brightness characterizes a color.

closing A dilation followed by an erosion. A closing fills small holes in objects and

smooths the boundaries of objects.

clustering A technique where the image is sorted within a discrete number of classes

corresponding to the number of phases perceived in an image. The gray values and a barycenter are determined for each class. This process is repeated until a value is obtained that represents the center of mass for each phase or class.

CLUT Color lookup table. A table for converting the value of a pixel in an image

into a red, green, and blue (RGB) intensity.

color image An image containing color information, usually encoded in the RGB form.

color space The mathematical representation for a color. For example, color can be

described in terms of red, green, and blue; hue, saturation, and luminance; or hue, saturation, and intensity.

complex image Stores information obtained from the FFT of an image. The complex

numbers that compose the FFT plane are encoded in 64-bit floating-point values: 32 bits for the real part and 32 bits for the imaginary part.

connectivity Defines which of the surrounding pixels of a given pixel constitute its

neighborhood.

National Instruments IMAQTM User Manual

Specifications and Main Features

Frequently Asked Questions

User Manual

IMAQ Vision for Visual Basic User Manual

Support

Worldwide Technical Support and Product Information

National Instruments Corporate Headquarters

Worldwide Offices

Important Information

Warranty

Copyright

Trademarks

WARNING REGARDING USE OF NATIONAL INSTRUMENTS PRODUCTS

Contents

About This Manual

Conventions

Related Documentation

IMAQ Vision

NI Vision Assistant

NI Vision Builder for Automated Inspection

Other Documentation

About IMAQ Vision

Documentation and Examples

IMAQ Vision for Visual Basic Organization

cwimaq.ocx

CWIMAQ Control

CWIMAQVision Control

CWIMAQViewer Control

niocr.ocx

NIOCR control

cwmv.ocx

CWMachineVision control

ActiveX Objects

Creating IMAQ Vision Applications

Figure 1-1. General Steps for Designing a Vision Application

Figure 1-2. Inspection Steps for Building a Vision Application

Set Up Your Imaging System

Calibrate Your Imaging System

Create an Image

Acquire or Read an Image

Acquiring an Image

One-Shot Acquisition

Continuous Acquisition

Reading a File

Converting an Array to an Image

Display an Image

Attach Calibration Information

Analyze an Image

Improve an Image

Lookup Tables

Filters

Convolution Filter

Nth Order Filter

Grayscale Morphology

Complex Image Operations

Define Regions of Interest

Figure 3-1. Steps to Taking Grayscale and Color Measurements

Defining Regions Interactively

Table 3-1. Tools Palette Functions

Figure 3-2. Tools Information

Defining Regions Programmatically

Table 3-2. Methods that Add Shapes to Regions

Defining Regions with Masks

Measure Grayscale Statistics

Measure Color Statistics

Figure 3-3. Primary Components of an 32-bit Color Image

Figure 3-4. Primary Components of a 64-bit Color Image

Comparing Colors

Learning Color Information

Specifying the Color Information to Learn

Figure 3-5. Template Color Information

Using the Entire Image

Figure 3-6. Using the Entire Image to Learn Color Distribution

Figure 3-7. Using a Single Region to Learn Color Distribution

Figure 3-8. Using Multiple Regions to Learn Color Distribution

Choosing a Color Representation Sensitivity

Ignoring Learned Colors

Create a Binary Image

Figure 4-1. Steps for Performing Particle Analysis